Post on 31-Jan-2017
Research ArticleResearch on Online Moisture Detector in GrainDrying Process Based on VF Conversion
Zhe Liu1 Zidan Wu2 Zhongjie Zhang2 Wenfu Wu1 and Hexin Li3
1 College of Biological amp Agricultural Engineering Jilin University Changchun 130022 China2 Academy of State Administration of Grain Baiwanzhuang Street 11 Xicheng District Beijing 100037 China3The College of Engineering and Technology Jilin Agricultural University Changchun 130118 China
Correspondence should be addressed to Wenfu Wu wwfzlb126com
Received 18 March 2014 Revised 20 August 2014 Accepted 6 September 2014
Academic Editor Ping-Lang Yen
Copyright copy 2015 Zhe Liu et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
An online resistance grain moisture detector is designed based on the model of the relationship between measurement frequencyand grainmoisture and the nonlinear correctionmethod of temperatureThe detector consists of lower computer the core functionof which is the sensing of grain resistance values which is based on VF conversion and upper computer the core function of whichis the conversion of moisture and frequency and the nonlinear correction of temperature The performance of the online moisturedetector is tested in a self-designed experimental system the test and analysis results indicate that the precision and stability of thedetector can reach the level of the similar products which can be still improved
1 Introduction
Grain moisture content is one of the key indexes of grainstorage security and it is an important control index inthe process of grain processing The moisture content ofnew harvest grain is generally beyond the safe moisturecontent and thus is required to be dried Real-time onlinedynamic measurement of grain moisture content is theprecondition of the automatic control of all kinds of dryersin drying process But in practice the measurement stabilityof online grain moisture detector is unsatisfactory due tothe influence of several parameters such as variety regionenvironmental temperature and humidity [1ndash3] Thereforethe online measurement of grain moisture has been aproblem of the realization of automatic operation of graindryers
There are many methods of grain moisture detectionincluding drying method resistance method capacitancemethod microwave method and neutron method [4]Among the traditional grain moisture detection methodsldquooven-dryingmethodrdquo iswidely usedThismethod is accurateand time and electricity consuming and it is only suitable forlaboratory to use and thus cannotmeet the needs of the onlinemoisture detection [5] In recent years capacitive moisture
detection method developed rapidly but the measurementvalue is not only sensitive to temperature but also very sen-sitive to the grain flow velocity and grain compactness in thedryer andmany other factors in grainmoisturemeasurementAnd this method is limited to the sensor recalibration afterlong time use [6] Microwave method and neutron methodhave many advantages such as high accuracy fast speednoncontact noninvasive and nondamage measurement andthey easily realize the measurement of the grain internalmoisture But the measurement device is complex and highin price [7 8]
The self-designed online resistance grain moisture detec-tor is a combination of signal detection acquisition andprocessing To a certain extent it has overcome manyshortcomings of traditional ways such as long measurementperiod low precision poor stability complex measurementsystem and high cost And the nonlinear correction math-ematical model of temperature compensation is applied tothe process of data processing [9] The purpose of this paperis to improve the measurement precision and stability of themoisture detector through improving the design of moisturedetector hardware circuit and the data processing methodand thus meet the needs of moisture online detection andmoisture automatic control in grain drying process
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015 Article ID 565764 10 pageshttpdxdoiorg1011552015565764
2 Mathematical Problems in Engineering
Upper computer(display and control instrument panel)
Lower computer(determination part)
The power line The temperature sensor wire
Control panel line
Figure 1 The structure of moisture detector
2 Measuring Principle
Experimental studies have shown that the explicit functionrelation exists between grain moisture and its resistance Theresistance characteristics of grain are shown as follows [10]
(1) In a certain range of grainmoisture content (about 9to 20) an approximately linear relationship betweenthe logarithm of the resistance and moisture contentis observed
(2) In the above moisture content range the grainresistance value varies greatly With the increase ofmoisture content the resistance changes from ashigh as dozens of megohm to less than 1 megohmHowever below or above this range the logarithmiclinearization error is larger
(3) Grain temperature has a significant effect on its resis-tance that is to say the equivalent resistance of graindecreases with the rise of temperature Experimentalstudies have shown that the impact on resistancewhen the temperature rises 1∘C is equivalent to thatwhen the moisture content increases 01 that is01 (moisture content)∘C at normal temperatureconditions(minus10∘Csim+50∘C)
As a result the correlation properties of grain resistanceprovide a design basis for the online resistance grainmoisturedetector on principle
3 The Design of the Sensor
31 Resistance Signal Conversion Unit Moisture detector isshown in Figure 1 including lower computer determinationpart and upper computer display and control instrumentLower computer mainly consists of two parts electrode rollerand shell Among them the electrode roller is a crush typeelectrode with fixed pressure The crush type electrode rolleris designed because a certain pressure needs to be exerted tocrush the grain particles In this way the resistance signal
which is detected by the electrode can reflect the internalmoisture of grain
When the moisture detector is working the motor in thesampling mechanism drives electrode roller to do oppositerotation Grains are squeezed by the roller electrodes Inthe grain drying process the roller squeezes the grainswhich scatter down from hoist hopper and the grain resis-tance is transported to the signal conditioning circuitry(including signal acquisition signal amplification and VFconversion circuit unit) to process The frequency signalswhich are positively related to the grain moisture content areobtained The single-chip microcomputer is used to collectthe frequency values Through the mathematical model ofmoisture calibration and temperature nonlinear correctionthe transformation of grainmoisture content during the graindrying process is measured in real time
Because the grain is approximately elliptical that is around intermediate and slight cuspidal poles therefore inthe process of extrusion the changing rule of the contactarea between the grain seeds and the electrode roller is thevariation process from zero to maximum then to minimumThe pressure on the grain is also a variation process fromminimum to maximum and then to minimum And the cor-responding resistance change curves of sampling circuit arefrom themaximum resistance (open circuit) to theminimum(maximum moisture content) and then to maximum (opencircuit) [11]Therefore the frequency values are collected fromminimum to maximum and then to minimumThe curves ofthe relationship between the measurement frequency valuesand measurement time when the grains with different mois-ture content pass through the roller are shown in Figure 2
32 The Signal Processing Achievement Unit The signal pro-cessing achievement unit includes the design of moisturedetector hardware circuit and software program The systemblock diagram is shown in Figure 3
The whole hardware circuit system consists of lowercomputer hardware and upper computer hardware circuit
Mathematical Problems in Engineering 3
0
500
1000
1500
2000
2500
3000
0 05 1 15 2 25 3
Sample time (s)C
olle
ctio
n fre
quen
cy (H
z)Figure 2The curves of the relationship between themeasurement frequency values and timewhen the grains with differentmoisture contentpass through the roller
Signal acquisition
Resistance
Single chip microcomputer
system
Roller rotation
GrainTemperature
Temperature measurement
Motor drive
PC single chip systemdata processing and display
Lower computersignal acquisition
Communicationinterface
Figure 3 The system block diagram
Modular design method is used in order to debug andrepair the circuit easily Lower computer hardware circuitincludes power supplymodule signal acquisitionmodule thesmallest single-chip microcomputer system single-phase ACmotor control module temperature measurement moduleand serial communication module The upper computerhardware circuit includes key and display module datastorage module and serial communication module Thehardware circuit block diagram is shown in Figure 4
Among them the signal acquisition module comprisesresistance detection circuit signal amplification circuit andVF conversion circuit The resistance detection circuitchanges the traditional divider circuit mode [12] and theresistance value of grain is measured by using a Wheat-stone bridge circuit according to the principle of the bridgebalance as shown in Figure 5 The measurement using thebridge method is of higher sensitivity and more accuracyMeanwhile in order to avoid unnecessary ac interferencethe filter capacitors are added to both ends of the bridgearms Compared to traditional AD conversion circuit VF
conversion circuit has its own filtering function the accuracyof the obtained digital signal is higher And there is onlyone interface which can complete frequency signal inputconveniently between the VF converter and single-chipmicrocomputer Its interface is simple and less hardwareresource is occupied [13]
Software design includes the lower and upper single-chipmicrocomputer system Single-chip microcomputer softwaresystem is also designed by the structured and modular pro-gram design method The program structure block diagramis shown in Figure 6 The system initialization is realized andeach subroutine is called according to the demand by themain program The moisture and temperature acquisition iscompleted by timer interrupt and counter interrupt moduleThe temperature and moisture values display is realizedby the display module The electrode roller of moisturedetector lower computer is driven by motor driver moduleCommunication and data transmission are implemented bythe communicationmoduleThe drying equipment control isachieved by the output control module
4 Mathematical Problems in Engineering
Resistance testing
Signal amplification
Single -phase AC motor control
Serial communications
Power module
Power module
Keys and display module
Data storage module
Relay control
Upper computerLower computer
Signal acquisition module
VF converter
Temperaturemeasurement module
STC89C52SCM
STC89C52SCM
Figure 4 The hardware circuit block diagram
U0Ui
Measuredgrain
Rx
R1 R2
R3 R4
Figure 5 The bridge circuit
According to the function of the moisture detector thework flow chart of upper single-chip microcomputer systemis shown in Figure 7
The program flow chart of lower computer system mainis shown in Figure 8 To analyze the time sequence ofprocedures the time sequence diagram is shown in Figure 9As can be seen by the time sequence diagram the executiontime of the sampling and the data processing are included inthe time of grain extrusion Doing this can savemeasurementtime effectively and improve measurement speed to meet thetest requirements
In the dynamic test data in order to eliminate the datarandom fluctuation some digital filtering algorithms arerequired in the process of data processing [14] In lower
computer data processing subroutine the methods of outliereliminating and moving average filtering are adopted toprocess data In addition the moisture detector measures 100grains and then gets a group of moisture values by calculatingthe average of 100 grain moisture values This method of cal-culating the average from large sample can avoid the randominterference and gross errors
4 Materials and Methods
41 TestMaterials The Jijing 66 rice which is produced in thenortheast area is selected as test sample which has full grainbright color no lesion no crack and no mildew
Mathematical Problems in Engineering 5
Software design module of upper computer system
Main program
Initialization subroutine
Keyboard scan subroutine
Digital display subroutine
EEPROM subroutine
Data processing subroutine
Serial communication subroutine
(a)
Software design module of lower computer system
Main program
Initialization subroutine
Temperature measurement subroutine
Motor drive subroutine
Signal acquisition subroutine
Data processing subroutine
Serial communication subroutine
(b)
Figure 6 MCU program structure block diagram
Free
Parameter setting
The setting value minus 1
The setting value plus 1
Storage setting
Command transmission(start moisture measurement )
Moisture measurement
Initialization
Command transmission(stop moisture measurement )
Grain number display
Grain moisture display
Null
Pressldquoonrdquo
Press ldquo rdquo
Pressldquo rdquo
PressPressldquosaverdquo
Data processing
Pressldquostoprdquo
Grain number = 100
Grain number lt 100
ldquosetrdquo
Figure 7 Work flow chart of the upper computer
42 Test Instruments and Equipment The experimentaldevice mainly consists of self-developed and designed grainmoisture comprehensive test system which is shown inFigure 10 self-designed online resistance grain moisturedetector which is installed at the slot in the hoist mountingplate and installation method as shown in Figure 11 PM-2500 type automatic single grain moisture detector which isproduced by Japanese Kett Company 202-00A type electricthermostatic ovenHX-100 type high speed crusher precisionelectronic balance and other equipment including aluminum
sample boxes with cover sample bags a small spoon and abrush labels
43 Test Method
A Self-developed and designed grain moisture compre-hensive test system simulates the working state of thecirculation grain dryer and grain circulates in the testbench under the action of hoist Moisture detectorrelies on measuring grain which scatters down from
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
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2 Mathematical Problems in Engineering
Upper computer(display and control instrument panel)
Lower computer(determination part)
The power line The temperature sensor wire
Control panel line
Figure 1 The structure of moisture detector
2 Measuring Principle
Experimental studies have shown that the explicit functionrelation exists between grain moisture and its resistance Theresistance characteristics of grain are shown as follows [10]
(1) In a certain range of grainmoisture content (about 9to 20) an approximately linear relationship betweenthe logarithm of the resistance and moisture contentis observed
(2) In the above moisture content range the grainresistance value varies greatly With the increase ofmoisture content the resistance changes from ashigh as dozens of megohm to less than 1 megohmHowever below or above this range the logarithmiclinearization error is larger
(3) Grain temperature has a significant effect on its resis-tance that is to say the equivalent resistance of graindecreases with the rise of temperature Experimentalstudies have shown that the impact on resistancewhen the temperature rises 1∘C is equivalent to thatwhen the moisture content increases 01 that is01 (moisture content)∘C at normal temperatureconditions(minus10∘Csim+50∘C)
As a result the correlation properties of grain resistanceprovide a design basis for the online resistance grainmoisturedetector on principle
3 The Design of the Sensor
31 Resistance Signal Conversion Unit Moisture detector isshown in Figure 1 including lower computer determinationpart and upper computer display and control instrumentLower computer mainly consists of two parts electrode rollerand shell Among them the electrode roller is a crush typeelectrode with fixed pressure The crush type electrode rolleris designed because a certain pressure needs to be exerted tocrush the grain particles In this way the resistance signal
which is detected by the electrode can reflect the internalmoisture of grain
When the moisture detector is working the motor in thesampling mechanism drives electrode roller to do oppositerotation Grains are squeezed by the roller electrodes Inthe grain drying process the roller squeezes the grainswhich scatter down from hoist hopper and the grain resis-tance is transported to the signal conditioning circuitry(including signal acquisition signal amplification and VFconversion circuit unit) to process The frequency signalswhich are positively related to the grain moisture content areobtained The single-chip microcomputer is used to collectthe frequency values Through the mathematical model ofmoisture calibration and temperature nonlinear correctionthe transformation of grainmoisture content during the graindrying process is measured in real time
Because the grain is approximately elliptical that is around intermediate and slight cuspidal poles therefore inthe process of extrusion the changing rule of the contactarea between the grain seeds and the electrode roller is thevariation process from zero to maximum then to minimumThe pressure on the grain is also a variation process fromminimum to maximum and then to minimum And the cor-responding resistance change curves of sampling circuit arefrom themaximum resistance (open circuit) to theminimum(maximum moisture content) and then to maximum (opencircuit) [11]Therefore the frequency values are collected fromminimum to maximum and then to minimumThe curves ofthe relationship between the measurement frequency valuesand measurement time when the grains with different mois-ture content pass through the roller are shown in Figure 2
32 The Signal Processing Achievement Unit The signal pro-cessing achievement unit includes the design of moisturedetector hardware circuit and software program The systemblock diagram is shown in Figure 3
The whole hardware circuit system consists of lowercomputer hardware and upper computer hardware circuit
Mathematical Problems in Engineering 3
0
500
1000
1500
2000
2500
3000
0 05 1 15 2 25 3
Sample time (s)C
olle
ctio
n fre
quen
cy (H
z)Figure 2The curves of the relationship between themeasurement frequency values and timewhen the grains with differentmoisture contentpass through the roller
Signal acquisition
Resistance
Single chip microcomputer
system
Roller rotation
GrainTemperature
Temperature measurement
Motor drive
PC single chip systemdata processing and display
Lower computersignal acquisition
Communicationinterface
Figure 3 The system block diagram
Modular design method is used in order to debug andrepair the circuit easily Lower computer hardware circuitincludes power supplymodule signal acquisitionmodule thesmallest single-chip microcomputer system single-phase ACmotor control module temperature measurement moduleand serial communication module The upper computerhardware circuit includes key and display module datastorage module and serial communication module Thehardware circuit block diagram is shown in Figure 4
Among them the signal acquisition module comprisesresistance detection circuit signal amplification circuit andVF conversion circuit The resistance detection circuitchanges the traditional divider circuit mode [12] and theresistance value of grain is measured by using a Wheat-stone bridge circuit according to the principle of the bridgebalance as shown in Figure 5 The measurement using thebridge method is of higher sensitivity and more accuracyMeanwhile in order to avoid unnecessary ac interferencethe filter capacitors are added to both ends of the bridgearms Compared to traditional AD conversion circuit VF
conversion circuit has its own filtering function the accuracyof the obtained digital signal is higher And there is onlyone interface which can complete frequency signal inputconveniently between the VF converter and single-chipmicrocomputer Its interface is simple and less hardwareresource is occupied [13]
Software design includes the lower and upper single-chipmicrocomputer system Single-chip microcomputer softwaresystem is also designed by the structured and modular pro-gram design method The program structure block diagramis shown in Figure 6 The system initialization is realized andeach subroutine is called according to the demand by themain program The moisture and temperature acquisition iscompleted by timer interrupt and counter interrupt moduleThe temperature and moisture values display is realizedby the display module The electrode roller of moisturedetector lower computer is driven by motor driver moduleCommunication and data transmission are implemented bythe communicationmoduleThe drying equipment control isachieved by the output control module
4 Mathematical Problems in Engineering
Resistance testing
Signal amplification
Single -phase AC motor control
Serial communications
Power module
Power module
Keys and display module
Data storage module
Relay control
Upper computerLower computer
Signal acquisition module
VF converter
Temperaturemeasurement module
STC89C52SCM
STC89C52SCM
Figure 4 The hardware circuit block diagram
U0Ui
Measuredgrain
Rx
R1 R2
R3 R4
Figure 5 The bridge circuit
According to the function of the moisture detector thework flow chart of upper single-chip microcomputer systemis shown in Figure 7
The program flow chart of lower computer system mainis shown in Figure 8 To analyze the time sequence ofprocedures the time sequence diagram is shown in Figure 9As can be seen by the time sequence diagram the executiontime of the sampling and the data processing are included inthe time of grain extrusion Doing this can savemeasurementtime effectively and improve measurement speed to meet thetest requirements
In the dynamic test data in order to eliminate the datarandom fluctuation some digital filtering algorithms arerequired in the process of data processing [14] In lower
computer data processing subroutine the methods of outliereliminating and moving average filtering are adopted toprocess data In addition the moisture detector measures 100grains and then gets a group of moisture values by calculatingthe average of 100 grain moisture values This method of cal-culating the average from large sample can avoid the randominterference and gross errors
4 Materials and Methods
41 TestMaterials The Jijing 66 rice which is produced in thenortheast area is selected as test sample which has full grainbright color no lesion no crack and no mildew
Mathematical Problems in Engineering 5
Software design module of upper computer system
Main program
Initialization subroutine
Keyboard scan subroutine
Digital display subroutine
EEPROM subroutine
Data processing subroutine
Serial communication subroutine
(a)
Software design module of lower computer system
Main program
Initialization subroutine
Temperature measurement subroutine
Motor drive subroutine
Signal acquisition subroutine
Data processing subroutine
Serial communication subroutine
(b)
Figure 6 MCU program structure block diagram
Free
Parameter setting
The setting value minus 1
The setting value plus 1
Storage setting
Command transmission(start moisture measurement )
Moisture measurement
Initialization
Command transmission(stop moisture measurement )
Grain number display
Grain moisture display
Null
Pressldquoonrdquo
Press ldquo rdquo
Pressldquo rdquo
PressPressldquosaverdquo
Data processing
Pressldquostoprdquo
Grain number = 100
Grain number lt 100
ldquosetrdquo
Figure 7 Work flow chart of the upper computer
42 Test Instruments and Equipment The experimentaldevice mainly consists of self-developed and designed grainmoisture comprehensive test system which is shown inFigure 10 self-designed online resistance grain moisturedetector which is installed at the slot in the hoist mountingplate and installation method as shown in Figure 11 PM-2500 type automatic single grain moisture detector which isproduced by Japanese Kett Company 202-00A type electricthermostatic ovenHX-100 type high speed crusher precisionelectronic balance and other equipment including aluminum
sample boxes with cover sample bags a small spoon and abrush labels
43 Test Method
A Self-developed and designed grain moisture compre-hensive test system simulates the working state of thecirculation grain dryer and grain circulates in the testbench under the action of hoist Moisture detectorrelies on measuring grain which scatters down from
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
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Mathematical Problems in Engineering
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Differential EquationsInternational Journal of
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International Journal of
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Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Decision SciencesAdvances in
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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 3
0
500
1000
1500
2000
2500
3000
0 05 1 15 2 25 3
Sample time (s)C
olle
ctio
n fre
quen
cy (H
z)Figure 2The curves of the relationship between themeasurement frequency values and timewhen the grains with differentmoisture contentpass through the roller
Signal acquisition
Resistance
Single chip microcomputer
system
Roller rotation
GrainTemperature
Temperature measurement
Motor drive
PC single chip systemdata processing and display
Lower computersignal acquisition
Communicationinterface
Figure 3 The system block diagram
Modular design method is used in order to debug andrepair the circuit easily Lower computer hardware circuitincludes power supplymodule signal acquisitionmodule thesmallest single-chip microcomputer system single-phase ACmotor control module temperature measurement moduleand serial communication module The upper computerhardware circuit includes key and display module datastorage module and serial communication module Thehardware circuit block diagram is shown in Figure 4
Among them the signal acquisition module comprisesresistance detection circuit signal amplification circuit andVF conversion circuit The resistance detection circuitchanges the traditional divider circuit mode [12] and theresistance value of grain is measured by using a Wheat-stone bridge circuit according to the principle of the bridgebalance as shown in Figure 5 The measurement using thebridge method is of higher sensitivity and more accuracyMeanwhile in order to avoid unnecessary ac interferencethe filter capacitors are added to both ends of the bridgearms Compared to traditional AD conversion circuit VF
conversion circuit has its own filtering function the accuracyof the obtained digital signal is higher And there is onlyone interface which can complete frequency signal inputconveniently between the VF converter and single-chipmicrocomputer Its interface is simple and less hardwareresource is occupied [13]
Software design includes the lower and upper single-chipmicrocomputer system Single-chip microcomputer softwaresystem is also designed by the structured and modular pro-gram design method The program structure block diagramis shown in Figure 6 The system initialization is realized andeach subroutine is called according to the demand by themain program The moisture and temperature acquisition iscompleted by timer interrupt and counter interrupt moduleThe temperature and moisture values display is realizedby the display module The electrode roller of moisturedetector lower computer is driven by motor driver moduleCommunication and data transmission are implemented bythe communicationmoduleThe drying equipment control isachieved by the output control module
4 Mathematical Problems in Engineering
Resistance testing
Signal amplification
Single -phase AC motor control
Serial communications
Power module
Power module
Keys and display module
Data storage module
Relay control
Upper computerLower computer
Signal acquisition module
VF converter
Temperaturemeasurement module
STC89C52SCM
STC89C52SCM
Figure 4 The hardware circuit block diagram
U0Ui
Measuredgrain
Rx
R1 R2
R3 R4
Figure 5 The bridge circuit
According to the function of the moisture detector thework flow chart of upper single-chip microcomputer systemis shown in Figure 7
The program flow chart of lower computer system mainis shown in Figure 8 To analyze the time sequence ofprocedures the time sequence diagram is shown in Figure 9As can be seen by the time sequence diagram the executiontime of the sampling and the data processing are included inthe time of grain extrusion Doing this can savemeasurementtime effectively and improve measurement speed to meet thetest requirements
In the dynamic test data in order to eliminate the datarandom fluctuation some digital filtering algorithms arerequired in the process of data processing [14] In lower
computer data processing subroutine the methods of outliereliminating and moving average filtering are adopted toprocess data In addition the moisture detector measures 100grains and then gets a group of moisture values by calculatingthe average of 100 grain moisture values This method of cal-culating the average from large sample can avoid the randominterference and gross errors
4 Materials and Methods
41 TestMaterials The Jijing 66 rice which is produced in thenortheast area is selected as test sample which has full grainbright color no lesion no crack and no mildew
Mathematical Problems in Engineering 5
Software design module of upper computer system
Main program
Initialization subroutine
Keyboard scan subroutine
Digital display subroutine
EEPROM subroutine
Data processing subroutine
Serial communication subroutine
(a)
Software design module of lower computer system
Main program
Initialization subroutine
Temperature measurement subroutine
Motor drive subroutine
Signal acquisition subroutine
Data processing subroutine
Serial communication subroutine
(b)
Figure 6 MCU program structure block diagram
Free
Parameter setting
The setting value minus 1
The setting value plus 1
Storage setting
Command transmission(start moisture measurement )
Moisture measurement
Initialization
Command transmission(stop moisture measurement )
Grain number display
Grain moisture display
Null
Pressldquoonrdquo
Press ldquo rdquo
Pressldquo rdquo
PressPressldquosaverdquo
Data processing
Pressldquostoprdquo
Grain number = 100
Grain number lt 100
ldquosetrdquo
Figure 7 Work flow chart of the upper computer
42 Test Instruments and Equipment The experimentaldevice mainly consists of self-developed and designed grainmoisture comprehensive test system which is shown inFigure 10 self-designed online resistance grain moisturedetector which is installed at the slot in the hoist mountingplate and installation method as shown in Figure 11 PM-2500 type automatic single grain moisture detector which isproduced by Japanese Kett Company 202-00A type electricthermostatic ovenHX-100 type high speed crusher precisionelectronic balance and other equipment including aluminum
sample boxes with cover sample bags a small spoon and abrush labels
43 Test Method
A Self-developed and designed grain moisture compre-hensive test system simulates the working state of thecirculation grain dryer and grain circulates in the testbench under the action of hoist Moisture detectorrelies on measuring grain which scatters down from
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
4 Mathematical Problems in Engineering
Resistance testing
Signal amplification
Single -phase AC motor control
Serial communications
Power module
Power module
Keys and display module
Data storage module
Relay control
Upper computerLower computer
Signal acquisition module
VF converter
Temperaturemeasurement module
STC89C52SCM
STC89C52SCM
Figure 4 The hardware circuit block diagram
U0Ui
Measuredgrain
Rx
R1 R2
R3 R4
Figure 5 The bridge circuit
According to the function of the moisture detector thework flow chart of upper single-chip microcomputer systemis shown in Figure 7
The program flow chart of lower computer system mainis shown in Figure 8 To analyze the time sequence ofprocedures the time sequence diagram is shown in Figure 9As can be seen by the time sequence diagram the executiontime of the sampling and the data processing are included inthe time of grain extrusion Doing this can savemeasurementtime effectively and improve measurement speed to meet thetest requirements
In the dynamic test data in order to eliminate the datarandom fluctuation some digital filtering algorithms arerequired in the process of data processing [14] In lower
computer data processing subroutine the methods of outliereliminating and moving average filtering are adopted toprocess data In addition the moisture detector measures 100grains and then gets a group of moisture values by calculatingthe average of 100 grain moisture values This method of cal-culating the average from large sample can avoid the randominterference and gross errors
4 Materials and Methods
41 TestMaterials The Jijing 66 rice which is produced in thenortheast area is selected as test sample which has full grainbright color no lesion no crack and no mildew
Mathematical Problems in Engineering 5
Software design module of upper computer system
Main program
Initialization subroutine
Keyboard scan subroutine
Digital display subroutine
EEPROM subroutine
Data processing subroutine
Serial communication subroutine
(a)
Software design module of lower computer system
Main program
Initialization subroutine
Temperature measurement subroutine
Motor drive subroutine
Signal acquisition subroutine
Data processing subroutine
Serial communication subroutine
(b)
Figure 6 MCU program structure block diagram
Free
Parameter setting
The setting value minus 1
The setting value plus 1
Storage setting
Command transmission(start moisture measurement )
Moisture measurement
Initialization
Command transmission(stop moisture measurement )
Grain number display
Grain moisture display
Null
Pressldquoonrdquo
Press ldquo rdquo
Pressldquo rdquo
PressPressldquosaverdquo
Data processing
Pressldquostoprdquo
Grain number = 100
Grain number lt 100
ldquosetrdquo
Figure 7 Work flow chart of the upper computer
42 Test Instruments and Equipment The experimentaldevice mainly consists of self-developed and designed grainmoisture comprehensive test system which is shown inFigure 10 self-designed online resistance grain moisturedetector which is installed at the slot in the hoist mountingplate and installation method as shown in Figure 11 PM-2500 type automatic single grain moisture detector which isproduced by Japanese Kett Company 202-00A type electricthermostatic ovenHX-100 type high speed crusher precisionelectronic balance and other equipment including aluminum
sample boxes with cover sample bags a small spoon and abrush labels
43 Test Method
A Self-developed and designed grain moisture compre-hensive test system simulates the working state of thecirculation grain dryer and grain circulates in the testbench under the action of hoist Moisture detectorrelies on measuring grain which scatters down from
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 5
Software design module of upper computer system
Main program
Initialization subroutine
Keyboard scan subroutine
Digital display subroutine
EEPROM subroutine
Data processing subroutine
Serial communication subroutine
(a)
Software design module of lower computer system
Main program
Initialization subroutine
Temperature measurement subroutine
Motor drive subroutine
Signal acquisition subroutine
Data processing subroutine
Serial communication subroutine
(b)
Figure 6 MCU program structure block diagram
Free
Parameter setting
The setting value minus 1
The setting value plus 1
Storage setting
Command transmission(start moisture measurement )
Moisture measurement
Initialization
Command transmission(stop moisture measurement )
Grain number display
Grain moisture display
Null
Pressldquoonrdquo
Press ldquo rdquo
Pressldquo rdquo
PressPressldquosaverdquo
Data processing
Pressldquostoprdquo
Grain number = 100
Grain number lt 100
ldquosetrdquo
Figure 7 Work flow chart of the upper computer
42 Test Instruments and Equipment The experimentaldevice mainly consists of self-developed and designed grainmoisture comprehensive test system which is shown inFigure 10 self-designed online resistance grain moisturedetector which is installed at the slot in the hoist mountingplate and installation method as shown in Figure 11 PM-2500 type automatic single grain moisture detector which isproduced by Japanese Kett Company 202-00A type electricthermostatic ovenHX-100 type high speed crusher precisionelectronic balance and other equipment including aluminum
sample boxes with cover sample bags a small spoon and abrush labels
43 Test Method
A Self-developed and designed grain moisture compre-hensive test system simulates the working state of thecirculation grain dryer and grain circulates in the testbench under the action of hoist Moisture detectorrelies on measuring grain which scatters down from
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
6 Mathematical Problems in Engineering
Start
Initialization subroutine
Temperature measurement subroutine
Waiting for the serial commandwhether to start
Motor drive subroutine
Motor stop
Data processing subroutine
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Interrupt return
Y
N
Interrupt return
Frequency acquisitionsubroutine
T0 and T1 interruptionsubroutine
Waiting for T0 and T1
interruption
Count gt critical value
Count [0] lt count [1] ampamp count [1] lt count [2]
Max count [0]middot middot middot count [14]
Call the serial communicationprogram and send out the counting
number and frequency values
Count lt critical value
The counting number =100
Start
Send initial timingvalue 50ms to T0
T0 = 50ms
Send the value inthe counter T1
count = counter
Counter reset(count = 0)
Start
Count(counter++)
Timer T0 Counter T1
Figure 8 Flow chart of lower computer main program
hoppers in the hoist to gain the changes of grainmoisture that is in the test bench
B Self-designed moisture detector works 12 hours con-tinuously in the test bench The electrode roller col-lects each 100 grains of rice then a group of frequencyvalues is processed calculated and displayed and thefrequency values are recorded
C Self-designed moisture detector measures each 5times and PM-2500 moisture detector measures onetime
D Every 1 hour the rice in the test bench is sam-pled The laboratory drying method is adoptedto measure the grain moisture content accordingto the moisture determination theory and specificoperating procedures in GBT5497-1985 ldquomethodfor determination of grain and oilseeds moisturecontentrdquo
E The test results of the self-designedmoisture detectorPM-2500 and laboratory drying method are com-pared
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 7
t
t
t
tRice grain extrusion time
Sampling time
Data processing time
t1001 t1002 t1003 t1004 t1100
t21 middot middot middot t215 t21 middot middot middot t215 t21 t22t21 middot middot middot t215 t21 middot middot middot t215
middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot
middot middot middot
middot middot middot
middot middot middot
t3001 t3004 t5t3002 t3003 t3100
t4001 t4002 t4003 t4100t4099
Serial transmition data time
Figure 9 The time sequence analysis diagram of MCU program
The back of thetesting system
and the moistermeter installation
Figure 10 Self-designed grain moisture comprehensive test system
5 Results and Analysis
51 Measurement Data Analysis Frequency measurementvalues of the self-designed moisture detector were com-pared with the actual moisture which was measured bythe laboratory method and the relationship between themwas shown in Figure 12 The mathematical model betweenmeasurement frequency and grain moisture content wasestablished through this diagram
52 The Formula Method of Nonlinear Temperature Com-pensation Calibration Because the temperature is one of
the main factors influencing the measurement accuracy ofgrain moisture content especially on the condition of highmoisture measurement the error reaches 6sim8 It cannotmeet the requirements of practical application and nonlinearcalibration of temperature must be conducted [15]
According to the test method of 33 the experiments werecarried out twice 1150 times measuring values of the firsttest and 850 times measuring values of the second test wererecorded Firstly the measurement values of the moisturedetector without temperature compensation calibration andthe results of laboratory drying method were comparedThe test results were shown in Figure 13 The error of thetest results was solved by 3 Sigma criteria In test one theerror between moisture detector measurement values andthe actual moisture was in minus0210sim0631 In test two theerror betweenmoisture detectormeasurement values and theactual moisture was in minus0636sim0758
As shown in Figure 14 the variation range of temperatureduring the process of test one was in 19∘Csim235∘C and thevariation range of temperature during the process of test twowas in 14∘Csim19∘C
Through the analysis of experimental data the parameterestimationmethodwas adopted and the nonlinear correctionformula of temperature was obtained after repeated calcula-tion and comparison Set up119872
1119872
2 119872
119899for the sample
of overall119872 As the distribution function of overall119872 theform of 119865(1198721015840 119905 119860 119861 119862119863 119864) is
119872 = (119860[
119872
1015840
119861 sin ((119905 + 119862) 119863)]
2
+ 119864) times 100 (1)
In the formula 119872 is the moisture value after tempera-ture compensation 1198721015840 is the measurement moisture value
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
8 Mathematical Problems in Engineering
Hoist
Moisture meter mountingplate (removable)
Temperature sensorinstallation hole
Moisture meterinstallation port
(a)
The inside of moisture meter mounting plate
Funnel
Slot
(b)
Figure 11 Schematic diagram of moisture detector installation position
02468
10121416182022
0 500 1000 1500 2000 2500 3000 3500 4000 4500Measuring frequency (Hz)
Act
ual m
oist
ure
()
Figure 12The relationship betweenmeasurement frequency valuesand the actual moisture
Comparison between moisture measurement values andthe actual values (test one)
101214161820222426
1 163 325 487 649 811 973 1135Number of measurement
Moi
stur
e (
)
(a)
Comparison between moisture measurement values andthe actual values (test two)
101214161820222426
1 89 177 265 353 441 529 617 705 793Number of measurement
Moi
stur
e(
)
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 13 Comparison between moisture detector measurementvalues and the laboratory method measurement values
Measurement temperature (test one)
1012141618202224262830
1 88 175 262 349 436 523 610 697 784 871 958 10451132Number of measurement
Tem
pera
ture
(∘C)
(a)
Number of measurement
Measuring temperature
1012141618202224262830
1 95 189 283 377 471 565 659 753
Tem
pera
ture
(∘C)
(b)
Figure 14 The temperature variation in tests
before temperature compensation 119905 is the temperature value119860 119861 119862 119863 and 119864 are the estimated parameters and119872
1015840
1
119872
1015840
2
119872
1015840
119899
1199051 119905
2 119905
119899are the corresponding sample
observation valuesFrequency values and temperature values were collected
by themoisture detector an appropriate statistic that includes
119860(119872
1119872
2 119872
119899) 119861(119872
1119872
2 119872
119899) 119862(119872
1119872
2 119872
119899)
119863(119872
1119872
2 119872
119899) and 119864(119872
1119872
2 119872
119899) was con-
structed The observed values 1198721015840(1199091 119909
2 119909
119899) and 119905(119909
1
119909
2 119909
119899) were used to estimate the values of parameters 119860
119861 119862119863 and 119864The measurement moisture values curves after tempera-
ture nonlinear correction were shown in Figure 15 The errorof the test results was solved by 3 Sigma criteria In test onethe error of the moisture detector measurement values wasbetween minus0418 and 0256 In test two the error of themoisture detector was between minus0469 and 0527
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 9
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test one)
101214161820222426
1 137 273 409 545 681 817 953 1089Number of measurement
Moi
sture
()
(a)
Number of measurement
Comparison between moisture measurement value and the actualvalue after temperature compensation calibration (test two)
1 75 149 223 297 371 445 519 593 667 741101214161820222426
Moi
sture
()
Actual moisture measurement valuesSelf-designed moisture meter measurement valuesLinear (actual moisture measured values)
(b)
Figure 15 Comparison between moisture detector measurementvalues and the actual values after temperature compensation cali-bration
53 Comparison and Analysis The measurement resultsof the self-designed moisture detector and PM-2500 werecompared with laboratory drying method The results wereshown in Figure 16The linear tuning function on the displayand control instrument panel of PM-2500 was used to adjustthe measurement values to the actual moisture values asshown in Figure 17
The stability of the self-designed moisture detector andPM-2500 moisture meter was compared By the calculationin test one the stability of PM-2500 between minus05 and 05was within 7826 and the stability between minus1 and 1was within 9870 The stability of self-designed moisturemeter between minus05 and 05 was within 8209 andthe stability between minus1 and 1 was within 9983 Intest two the stability of PM-2500 between minus05 and 05was within 5625 and the stability between minus1 and 1was within 9563 The stability of self-designed moisturedetector between minus05 and 05 was within 7688 and thestability between minus1 and 1 was within 9525
6 Conclusion
(1) In this paper the online resistance grain moisturedetector is designed based on the model of the rela-tionship between measurement frequency and grainmoisture and the nonlinear correction method of
101214161820222426
1 135 269 403 537 671 805 939 1073Number of measurement
Moi
stur
e (
)
Comparison of two measurement methods (test one)
(a)
1 89 177 265 353 441 529 617 705 793Number of measurement
101214161820222426
Moi
stur
e (
)PM-2500 measurement valuesActual moisture measurement valuesSelf-designed moisture meter measurement values
Comparison of two measurement methods (test two)
Linear (actual moisture measured values)
(b)
Figure 16 Comparison of two methods measurement values
temperatureThe detector consists of lower computerthe core function of which is sensing of grain resis-tance values which is based on VF conversionand upper computer the core function of which isthe conversion of moisture and frequency and thenonlinear correction of temperature
(2) Experimental study about grain flow has been doneon the self-designed moisture testing system Themathematical model of the relationship between themeasurement frequency and grain moisture con-tent is thus established Meanwhile the nonlinearcalibration model of temperature compensation iscombined The performance of moisture detectorand the correctness and stability of the model havebeen tested The results showed that the error ofthe detector is between minus0469 and 0527 thestability between minus05 and 05 is within 7688and stability between minus1 and 1 is within 9525
(3) Through the experiment on self-designed moisturetest system the precision and the stability of theself-designed moisture detector and PM-2500 typeautomatic single grain moisture detector which isproduced by Japanese Kett Company were comparedThe results indicated that the precision and stabilityof the detector can reach the level of the similarproducts which can be still improved
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
10 Mathematical Problems in Engineering
Comparison of two moisture meters measurement valuesafter correction (test one)
10
12
14
16
18
20
22
24
26
1 139 277 415 553 691 829 967 1105
Number of measurement
Moi
sture
()
(a)
Comparison of two moisture meters measurement valuesafter correction (test two)
Number of measurement
10
12
14
16
18
20
22
24
26
Moi
sture
()
1 75 149 223 297 371 445 519 593 667 741
Self-designed moisture meter measurement valuesPM-2500 measurement values after correctionActual moisture measurement valuesLinear (actual moisture measured valued)
(b)
Figure 17 Comparison of two methods measurement values aftercalibration of PM2500
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by ldquoThe Research on GrainDrying Speed Control Model and Intelligent Systemrdquofor ldquoGrain Industry Scientific Research in the Public Interestrdquo(201313001-07)
References
[1] Z Yonglin W Wangping Z Changzheng Z Shengquan andX Hui ldquoIntelligent real-time on-line measuring system formoisture content during grain dryingrdquo Transactions from theChinese Society of Agricultural Engineering vol 23 no 9 pp137ndash140 2007
[2] T Zhaosheng N Lewei Z Haixia and Q Yang ldquoOn-linemeasurement system of grain dryer for monitoring moisturecontentrdquo Transactions of the Chinese Society of AgriculturalEngineering vol 20 no 5 pp 130ndash133 2004
[3] Q Li Y Gao D Zhang et al ldquoStudy on the on-line system ofmeasuring moisture content in grainrdquo Journal of AgriculturalMachinery vol 26 no 3 pp 80ndash84 1995
[4] T Zhaosheng L Kunhua T Ruiming et al ldquoComments onquick measurement of grain moisture contentrdquo Journal of theChinese Cereals and Oils Association vol 14 no 3 1999
[5] B Zhai H Guo andH Xu ldquoSynthetic analyse and developmentsurvey of moisture measuring technology of grainrdquo Journal ofShenyang University of Technology vol 2 no 5 pp 413ndash4162001
[6] W C Wang and Y Z Dai ldquoA grain moisture detecting systembased on capacitive sensorrdquo International Journal of DigitalContent Technology and its Applications vol 5 no 3 pp 203ndash209 2011
[7] K B Kim J H Kim C J Lee S H Noh andM S Kim ldquoSimpleinstrument for moisture measurement in grain by free-spacemicrowave transmissionrdquo The American Society of Agriculturaland Biological Engineers vol 49 no 4 pp 1089ndash1093 2006
[8] Y Yueqian W Jianping and W Chengzhi ldquoStudy on on-linemeasurement of grain moisture content by neutron gaugerdquoTransactions of the CSAE vol 5 no 16 pp 99ndash101 2000
[9] L Lan Study and Realization of Intelligent Arithmetic for GrainMoisture Measuring Jilin University 2005
[10] Z Yang K Lu and C Liu ldquoDesign of the intelligent humid-iometer to test grainrsquos damprdquo Journal of Electronic Measurementand Instrument vol 10 no 3 pp 64ndash67 1996
[11] C Li and H Ban ldquoA grain moisture content measurementmethod and devicerdquo China 2006101234613[P]
[12] C Li ldquoDesign and experiment of on-line moisture contentmetering device for paddy drying processrdquo Journal of Agricul-tural Machinery vol 39 no 3 pp 56ndash59 2008
[13] Z Yan ldquoData acquisition of pressure based on LM331 amp MCUrdquoElectronic Design Engineering vol 17 no 3 pp 95ndash97 2009
[14] H Kaiming ldquoResearch on the parameters of sliding averagingfor digital filteringrdquo Journal of Jimei University vol 11 no 4 pp381ndash384 2006
[15] Z YaqiuWWenfu andWGang ldquoNeural network temperaturecompensation for grain moisture detection system based onvirtual instrumentrdquo Journal of the Chinese Cereals and OilsAssociation vol 26 no 5 2011
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of