Adaptive Technologies EconomicOptimization and Process Control

An adaptive technologies economic optimization and advanced process control system f or variousunit operations in an ammonia plant was developed to increase the ammonia production rate and

decrease energy consumption of ammonia.

Esam Kharbat and Stephen A. MatthewsCallidus Technologies Inc., Tulsa, OK

Introduction

The ammonia producer owns an ammonia plantthat was originally designed to produce 1,100TPD (998 TPD (metric)). Over the years, the

plant has been upgraded to produce over 1,400 TPD(1,270 TPD (m)) (Figure 1). The original objective ofthe ammonia plant adaptive technologies economicoptimization and advanced process control project wasto improve the energy consumption (energy savings)of the plant. Energy savings have been achieved.However, significant production improvements werealso realized.

Adaptive technologies is a blend of old and newmodeling and advanced process control techniques.This new approach represents the next generation ofdynamic modeling and advanced process control thathas been successfully tested in many field applica-tions. As computer power grows and becomes moreaffordable, many CPU and memory intensive tech-nologies are becoming practical to run on the averagedesktop computer. It is this recent availability of low

cost/high power computer hardware that has madeadaptive technologies practical for today's advancedprocess control applications.

The blending of many different technologies toovercome shortcomings of an individual modelingmethodology is becoming a common practice in thehigh-tech modeling field. Mathematical techniquessuch as neural networks, genetic algorithms, fuzzylogic, and chaos theory are being used together tobuild practical solutions to today's instrument andcontrol problems. Adaptive technologies employ aunique combination of neural network and chaoticsystems algorithms to learn complex interactions ofprocess variables from historical production data.Fuzzy logic allows flexible, real world definition ofprocess operating constraints. Adaptive technologiesmodels are built using past process data and do notrequire physical properties as do first principlesmodels.

The adaptive technologies developed by PavilionTechnologies and used by Callidus Technologies rep-resent the next generation of dynamic modeling and

AMMONIA TECHNICAL MANUAL 320 1997

advanced process control software that has been testedin many field applications, with proven results. Theadaptive technologies employ a unique combination ofneural network and chaotic systems algorithms tolearn complex interactions of process variables fromhistorical production data. Fuzzy logic and fuzzy con-trol theory are also used to allow flexible, real worlddefinition of process operating constraints. This tech-nology has just recently become practical because ofthe low cost and high performance of new computers.

The most powerful feature of adaptive technologiesmodels is that these models can be trained on histori-cal process data, rather than from special plant testssuch as process control step tests or designed experi-ments as required by other control modeling tech-niques. Another unique feature of the adaptive tech-nologies models is that physical properties data is nota requirement, since the models are developed fromthe process data.

The ammonia producer contracted a senior advancedprocess control consultant to determine how to obtainan ammonia plant dataset from their existing FisherDC2 and a Westronics Digital Temperature indicator.The dataset was obtained by attaching a personal com-puter to the Fisher DC2 printer port and purchasing a$500 PC circuit board for the Westronics temperatureindicator. A "C" language program was developed byPavilion Technologies to convert the PC dataset for-mat to one that was readable by Process Insights TM.The dataset contained 210 measured variables, flows,temperatures, pressures, speeds, and analyses.

Description of Implementation

The term "adaptive technologies" is used as a gener-al term to encompass neural networks, fuzzy logic,chaotic systems algorithms, and other mathematicalprocedures to predict or learn information aboutprocesses from the historical data. This term is usedthroughout this article.

Adaptive technologies is especially valuable in sys-tems where the following conditions exist:

« Operating systems where the production rates,energy consumption and quality control have a nonlin-ear relationship to the controlled variables such astemperatures, pressures, analysis, and so on.

« Plant systems where there is a long residence time(hours) between the time a control variable is manipu-lated, and the desired results are observed. High puritydistillations, large reactors, and urea plants are goodexamples of long residence time processes.

» Process systems where it is very difficult to get thephysical properties of the feed and product streams.Adaptive technologies use only the process data, anddo not require physical properties.

• Reactor systems where the main reactions takeplace and many side reactions also occur. In otherchemical engineering modeling techniques, the sidereactions are estimated with tuning factors or notincluded at all. Since adaptive technologies models aretrained on process data, the side reactions are takeninto account hi the training procedure.

• Control systems where it is very difficult to get ahardware analyzer to give reliable results. Soft sensoranalyzers can be employed in these applications.

The project described in this article used a sevenphase program to implement the project. This sevenphase program was condensed into a five phase pro-gram in January 1996. An ammonia producer can takeadvantage of an adaptive technologies project throughthe use of the five phase project. They are:

Phase 1: Demonstration Model Development andBenefits Report.

Phase 2: Model implementation (system specifica-tion and installation).

Phase 3: Operations model development - open loopcontrol.

Phase 4: System implementation - closed loop con-trol.

Phase 5: Post audit.A brief description of these five phases follows.

Phase 1: Demonstration model development andbenefits report

The following information was requested from theclient for demonstration model development:

(1) Process and instrument drawings.(2) Engineering reports that describe the ammonia

plant operation.(3) Laboratory data.(4) Product specifications.

AMMONIA TECHNICAL MANUAL 321 1997

Natural Gas

Supply

Process AirCO ShiftConverters

CO2Purification

HighTemperature ^^

AmmoniaStorageTank

Engineer Historian

iftl~—«v * *Adaptive Technologies

Software

[System Integration]

Figure 1. Ammonia plant flow. Figure 2. Adaptive technologies instrumentation:control system.

AMMONIAPLANT SYNTHESIS GAS CONVERTER QUENCHcoNvammiacs UAV

u^msso« B» 55S3Ä-OOKVUIEROIJT1«»«% 4J.745S »J7%

MK-MBQMQÜESCH

VMM

II

sou»VOMDIG.*

aMCJI

BZD3QOENCBSWOM

! «.74*1SUO

I 77t»I DIG.»

-UUOOBI-MA«LAHM

-uuoMODI-MAKA1ARM

-14UO(MODC-HANiALARM

CONVQUUtDfU4H-UI

CONVERTER SLOW OH ÜBST 1BB£C BZDS KntOHINMJJE TO OD1SISZ O7BZD

•a-uenmiu-uiEEDiTIIJl

BED1CK74tM»BO»D)71TII-M»

n-ui

BE09DC711n-145IED3DI7S4IM«BEDIODTiSIM47aotomtaTM4I

BED4IH7»TM49ÜED4DI771IHM80140001nuiBE&4ODTSOu-iaED) < IN TUnuiBEP4OOTMI11-154

CONVEKTEEODT«!TI-IM

CONVZKTCRFLOWO«BES4ISODT3DZTO IMSniE OFBEQ

It»

PRZDICUD PROD 1«J

ESHMATEDPBODl«i

73.67

TiOO

ActualTPD

68.00 70X» 7100 73.67

Figure 3. Synthesis gas converter quench valve con-trol.

Figure 4. Ammonia production rate actual vs.predicted.

AMMONIA TECHNICAL MANUAL 322 1997

(5) Instrument and control system description.(6) Description of the instrumentation—computer

system.(7) List of economic incentives for improved con-

trol.(8) Any other information or reports that can be used

by the Callidus engineers to better understand theprocess.

The previous information was used to prepare for atwo to three day visit to the plant to obtain additionalinformation about the plant and to discuss the develop-ment of the adaptive technologies models. The prima-ry purpose of the visit was to design and obtain adataset for the models. The success of an adaptivetechnologies economic optimization and advancedprocess control project for an ammonia plant relies onone single item called a dataset. In fact, to be morespecific, a large (6,000 rows+) accurate dataset for theammonia plant is needed.

A dataset is defined as a large magnetic media datafile that has the format of an ASCII large spreadsheetas shown in Table 1.

The dataset contained 10,000 rows of one minutedata in two data files - one from the Fisher DC2 andthe other from the Westronics temperature indicator.This represented approximately one week of data.

The adaptive technologies modeling tool is capableof handling very large volumes of data. Datasets con-taining several years worth of plant data have beenused. The two data files were merged into one dataset

file. After the Fisher DC2 was replaced with the Fisher-Rosemount PROVOX CHIP system, new datasetswere produced from 27 data files, each of which con-tained nine variables. The files were easily merged (bytime) into one dataset file by using the software pre-processor. These new datasets were used as the basisfor the operations models.

The newer ammonia plants (late 1980s to 1995) typ-ically have some kind of distributed control system(DCS) or supervisory computer system where theabove dataset can be obtained fairly easily from theDCS historical files or the supervisory computer his-torical files. Figure 2 shows a typical system needed tosupport advanced process control. In the older ammo-nia plants, the instrument-control system consists ofpneumatic and/or electronic instrumentation. Some ofthe older plants had the first vintage of DCS, whichdid not contain history modules. The initial Fisher DC2

described in this article is just such a system.To convert the older plants to a DCS system typical-

ly costs $500,000 to $900,000, which is often difficultto justify, if the existing system is performing satisfac-torily. However, there is an interim solution to the datagathering problem. A pneumatic and/or electronicmultiplexer can be installed, with a personal computer(PC) to obtain the dataset. This installation is in therange of $30,000 to $50,000. This pneumatic and/orelectronic instrumentation-computer system wouldprovide the same history and graphical features of theDCS. The historical data from the system would be

Table 1. Adaptive Technologies Example Dataset Format

Description or Tag No. Date

2-202-202-202-202-20

•a

2-252-252-25

Time

10:0110:0210:0310:0410:05

9

9

8:028:028:02

Process GasFR-1 MMCF/H

75.375.876.275.476.6

•0

79.279.279.2

Primary Out 1TR-32°F

1,4791,4801,4851,4861,482

9

*

1,4911,4911,491

Loop PressurePR-35 psig

2,0052,0062,0082,0102,008

••

2,0112,0112,011

Variables NN=200+F,T,A,etc.

NNN.NNNNN.NNNNN.NNNNN.NNNNN.NN

9

«NNN.NNNNN.NNNNN.NN

AMMONIA TECHNICAL MANUAL 323 1997

used to develop the adaptive technologies models. Asimilar pneumatic instrumentation-computer system isused to control an ammonia plant at another ammoniaproducer's location.

At a later date, when the pneumatic and/or electron-ic instrument system is replaced with a DCS, the pneu-matic and/or electronic signal multiplexer might beconnected to the DCS-LAN (local area network) todecrease the cost of the DCS or installed in anotherplant to reclaim this investment. All the model devel-opment and computer programming would be recov-ered because the data would now come from the DCS-LAN rather than the pneumatic and/or signal multi-plexer.

The demonstration model development involved thefollowing:

• Establish the input and output variables for themodel.

• Format the dataset for the model.• Edit the dataset by using the model preprocessor.• Build the model using all the inputs and outputs.• Conduct the sensitivity analysis to determine the

most important variables.• Rebuild the model one or two times using a subset

of the total inputs based on the sensitivity analysis.• Build a control model.• Conduct several "what if' cases to determine how

the ammonia plant energy use can be decreased, pro-duction can be increased, and the unit efficienciesimproved.

After the demonstration models were developed, apresentation was conducted for the ammonia producerat the plant site. The benefits of production increasesand energy savings were shown and documented.

Phase 2: Model implementation (system specifi-cation and installation)

An overview of the tasks completed for the projectinstallation are as follows:

• Completion of the system analysis for thePROVOX system to be sure that the hardware wouldsupport the adaptive technologies project.

• System software requirements (operating systemevaluation) were satisfied by the PRO VOX-CHIPsystem. A special software interface was developed

between CHIP and the adaptive technologies software.• Instrument and control systems interface to a com-

puter was provided by the PROVOX system.• A list of additional instrumentation and controllers

needed to be connected to PROVOX were provided.The result of Phase 2 was a definition for the engi-

neering, hardware, software, and installation of thesystem to support the adaptive technologies applica-tion at the ammonia plant. A task list was created forthe project that provided the start time, completiontime, and the organization assigned to complete thetasks. The organizations that participated in the projectwere the ammonia producer, Callidus, and variousvendors.

The demonstration model was used to check theinstallation. The operator training was started at thistime. However, the majority of the training took placewhen the operations models were developed in Phase3. The difference between the demonstration modelsand operations models is the amount and accuracy ofthe data used to develop the models.

Phase 3: Operations model development - openloop control

In Phase 3, the adaptive technologies models wereturned over to the ammonia plant's operations person-nel. New models were built by the ammonia produc-er's instrument technicians with the help of theCallidus engineers. Callidus engineers also developedand updated both the prediction and control modelsthat were used for the open loop control. The accuracyof the models were established at this time, and theopen loop controls were implemented. In open loopcontrol, the adaptive technologies predicted controlsetpoints are checked and implemented by the opera-tors. These requested setpoints were displayed on theoperator's DCS console. In the case of the synthesisgas converter open loop control system, the newquench valve settings were updated every ten to twen-ty minutes.

Approximately one week was used to implement thesoftware interface between the adaptive technologiessoftware and the PROVOX CHIP system. During thistime, a DCS display was built for the operators.Figure 3 shows a copy of this display.

AMMONIA TECHNICAL MANUAL 324 1997

Figure 3 shows the face plates for the four quenchvalves DCS controllers, and one face plate for the con-verter outlet temperature, which has a limit of 630°F.The loop pressure and many of the individual catalyticbed temperatures are shown. The recommendedquench valve settings are shown just above the faceplates. The operator entered the new setpoints forquenches 1, 2 and 4. The quench setting for quench 3is within 1% of the suggested setpoint. The operatorhas elected to ramp this setpoint to the recommendedsetpoint of 49.74%.

The most important numbers on the display areshown in the lower righthand corner of the display,and are as follows:

DESIRED PROD 1,495PREDICTED PROD 1,465ESTIMATED PROD 1,446The DESIRED PROD of 1,495 TPD (1,647.5 metric

TPD) is the desired production that the operator wantsto achieve. When the operators want to achieve themaximum production rate, the desired production rateis set at some unattainable value. In this case, the1,495 TPD (1,647.5 metric TPD) is unattainablebecause some constraint such as maximum tempera-ture, maximum pressure, minimum temperature, andso on would be reached before the production rate wasachieved.

The PREDICTED PROD of 1,465 TPD (1,329 met-ric TPD) is what the model predicts the ammonia plantwill produce, if all the operating conditions remain thesame. Anyone that has operated a plant knows thevariables within the plant will change from minute tominute, so the 1,465 TPD (1,329 metric TPD) willmost likely not be achieved in the next 10 or 20 min.However, the production will increase from the ESTI-MATED PROD of 1,446 TPD (1,312 metric TPD). Asmentioned above, the plant production rate wasincreased approximately 30 TPD (27 metric TPD)through the use of this synthesis gas converter DCSopen loop control system. It is expected that the pro-duction will increase even more as the other ammoniaunit operations (primary/secondary reformers, shiftconverters, CO2 gas removal, methanation, refrigera-tion, and so on) are put under adaptive technologieseconomic optimization and advanced process control.

Energy savings of approximately 0.1 MMBtu/ton

(0.4 Kg»cal/ton) of ammonia were achieved for thesynthesis gas converter open loop control. It is expect-ed that the other ammonia plant unit operationsadvanced process control systems will mainly result inadditional energy savings.

The primary/secondary reformers open loop controlwill be implemented in the near future. The adaptivetechnologies model indicated the production rate willincrease approximately 5 TPD (4.5 metric TPD) andthe energy savings achieved will be approximately 0.3MMBTU/ton (1.19 kg»cal/ton).

The ammonia producer has offered to demonstratethe synthesis gas converter open loop computer con-trol system to any other ammonia producer who isinterested in installing an adaptive technologies eco-nomic optimization and advanced process control sys-tem in their plant. Several ammonia producers havealready visited the plant.

Phase 4: System implementation - closed loopcontrol

The synthesis gas converter closed loop control wasmore time consuming to implement because of the fol-lowing:

* The control system sensor validation model wasimplemented.

• Sensor validation alarms, operational alarms, amodel down alarm (a watchdog timer) and other safetyfeatures were configured in the control system.

» Plant dynamics were taken into account. Openloop controls allowed the operators to handle plantdynamics.

The setpoints from the adaptive technologies modelare made directly to the ammonia plant instrument andcontrol systems. The operator does not check the set-points before they are implemented. For safety reasonsand to give operators a level of control over the com-puter control system, they needed the ability to turnoff the adaptive technologies control system. Normalrate and constraint control were used to handle plantdynamics for closed loop control. The results of closedloop control are the highest possible production ratesand energy savings for an ammonia plant.

The safety aspects of the closed loop step are moni-tored very closely through a sensor validation model.

AMMONIA TECHNICAL MANUAL 325 1997

The sensor validation model evaluates the 20+ keyvariables that are used in the synthesis gas convertermodel for accuracy and range. If either of these para-meters are found to be in error for a single variable,the sensor validation model program submits an esti-mated value to the control model. This estimated valuecomes from an analysis of the other valid variables.The program also gives the maximum number of vari-ables that can fail before the model has to be taken off-line. This extra precaution is taken because the com-puter adjusts the setpoints without the aid of the opera-tor.

For the purpose of safety, a watchdog timer is con-figured in the control system. This feature returns thecontrol system to the normal operator control mode,prior to computer control, if the computer control pro-gram fails. The operators will then control the unitunder DCS control, again, as they did before the com-puter control was implemented.

The result of the synthesis gas converter closed loopcomputer control is a production rate increase ofapproximately 10 TPD (9 TPD (metric)) and an energysavings of approximately 0.1 MMBtu/ton (0.4kg»cal/ton) of ammonia. The open loop production of30 TPD (27 metric TPD) was determined by operatingexperience over a few months. The 10 TPD (9 metricTPD) were estimated from a designed experimentwhere the operators ran the plant for three days onopen loop control. Then, the plant was run for threedays on closed loop control. The 10 TPD (9 metricTPD) and the 0.1 MMBtu/ton (0.4 kg»cal/ton) ofammonia approximations were determined from thistest.

The primary/secondary reformers closed loop con-trol is in the process of being implemented. This con-trol system will adjust the primary reformer MICspasses.

Phase 5: Post audit

The most important post audit event is the ammoniaplant production rate increase from the range of 1,390to 1,420 TPD (1,261 to 1,288.5 metric TPD) in May1994 to 1,440 to 1,460 TPD (1,306.5 to 1,325 metricTPD) in July 1995. The increased production rateswere due to better plant operations and the adaptive

technologies economic optimization and advancedprocess control project.

The post audit program will continue as moreammonia plant unit operations are put on closed loopcontrol. The adaptive technologies economic opti-mization and advanced process control project eventsincluded obtaining a dataset for the entire ammoniaplant, developing a model for the entire ammoniaplant, developing a model particular to the synthesisgas converter, upgrading the entire ammonia plantpneumatic system to the DCS, implementing the syn-thesis gas converter in open loop advisory computercontrol and then closed loop control, and finally devel-oping the primary/secondary reformer model.

The synthesis gas converter control model wasdeveloped and implemented in a period of 17 months.This is due to the fact that this project was the first ofits kind in an ammonia plant. Both the client and theconsultant spent enough time to develop, implement,and validate the models. Also, the ammonia plant wasupgraded from pneumatic instrumentation to distrib-uted control system, a project which took about sevenmonths to complete. Currently, adaptive technologiesprojects are being executed and completed in about sixmonths. This includes dataset development, demon-stration model development, operational model devel-opment, and finally model implementation in openloop and closed loop computer control.

As to why it took approximately 17 months toimplement the synthesis gas converter open loop con-trol, some of the reasons are as follows:

(1) This was the first synthesis gas converter modelever developed for an ammonia plant.

(2) It took a couple of months to figure out how toget a dataset from the Fisher DC2 and Westronic tem-perature indicator.

(3) The adaptive technologies model helps justifythe installation of a new Fisher-Rosemount PRO VOXsystem. This installation delayed the advanced processcontrol project approximately seven months.

(4) The ammonia plant operations department decid-ed to take plenty of time to implement the new DCSbefore their operators were asked to take on theadvanced process control technology. This was a verygood decision, because when it was time to implementthe advanced process control, the operators accepted it

AMMONIA TECHNICAL MANUAL 326 1997

with enthusiasm.(5) The software was new and some modifications

had to be made before the advanced process controlsystem could be implemented.

(6) Limited manpower resources at both the ammo-nia plant and the consultants facilities.

(7) Delays due to standard operation of an ammoniaplant such as a hurricane, gas curtailment, turnaround,exceptionally cold weather, unscheduled shutdowns,and so on.

During the period of August 1995 to March 1996,the synthesis gas converter open loop control was onlytaken off-line four times. The reasons are as follows:

(1) The pressure sensor on the pipeline to the highpressure loop failed and caused the density compen-sated flow to produce bad data, which caused themodel recommended quench control outputs to be inerror. This would not have happened if the sensor vali-dation had been installed.

(2) A hurricane hit Louisiana and caused the gas tobe curtailed to the ammonia plant. The ammonia plantproduction rate went below 1,100 TPD (998 metricTPD) which was outside the dataset values.

(3) There were some operational problems with theprimary reformer such as a leak hi the boiler feedwatersection of the primary reformer, which caused someproblems with the synthesis gas converter model.

(4) The ambient weather temperature dropped below30°F and caused a gas curtailment which decreased theproduction rate to about 1,100 TPD (998 metric TPD).

Sensor validation has now been installed. This hascorrected items 1 and 3. During the last curtailment, adataset was taken and added to the operations modelset. A new RunTime model was developed. This hascorrected items 2 and 4. The model stayed on-line thenext time the production had to be lowered for a cur-tailment.

The closed loop control of the synthesis gas convert-er was delayed until March 1996 because of opera-tional problems within the plant. A turnaroundoccurred in the fall. The main reason for the delay,however, was to correct the open loop control modelas described earlier. This developed the operators'confidence in the system before the closed loop con-trol was commissioned. The synthesis gas converter iscurrently operating in closed loop control. The proce-

dures to implement the synthesis gas converter adap-tive technologies economic optimization and advancedprocess control project have been developed.Therefore, to implement a new project, it will take lessthan six months — if the hardware to conduct the pro-ject is in place and ready for the software installation.The schedule will also be impacted by typical plantoperations problems, and how quickly operators canlearn the new system.

Model Description

The datasets were built from 27 PRO VOX CHIPfiles. The CHIP system produces individual data filesthat consist of date and time stamps, tag names, plus 9variables (temperatures, pressures, flows, and so on).These nine variable tables are 6,000 rows long. The 27tables were merged, by time, into one dataset of 200+variables. To have datasets longer than 6,000 rows, theDEC VMS editor was used to append the additional27 files to the original 6,000 rows. This appendingprocedure was used several times to get the finaldataset for the models. An example format for adataset file is shown in Table 2.

Figure 4 shows the results of the synthesis gas con-verter model. If the model was 100% accurate, the ploton Figure 4 would be a 45° line. Since there is a verynarrow band of points around the 45° line, the accura-cy of the model is extremely good; the predicted val-ues are nearly identical to the actual values. A goodmodel has an R-error squared of 0.8 or better. Thismodel has shown an R-squared of greater than 0.90.

Performance of System

The performance of the system is shown in Tables 3and 4. The synthesis gas converter adaptive technolo-gies model indicated the ammonia plant productionrate would increase 24 TPD (22 metric TPD), if thesynthesis gas converter first catalytic bed was cooledby 10 to 20°F. The four catalytic beds are cooled byopening the quench valves or heated by closing thequench valves. The model results are in Table 3.

The plant results from the distributed control system(DCS) are shown in Table 4. This test was conductedduring normal operations, and was not a .designed test.

AMMONIA TECHNICAL MANUAL 327 1997

73SI

72.00

OutputValue

70.00 :

68.00 •••••(—•—«-s«..-»

St £!'•• 50.0

Percent Production Rate

100.0

Figure 5. Synthesis gas converter temperatures,H-N calculation, and loop pressure vs. production

rate.It is shown as an example of what can be done.

The change in operations resulted in a 10 TPD (9metric TPD) increase in production, which is equiva-lent to about $2,000 per day (approximately $660,000per year for a 330-day year), and the energy consump-tion (MMBtu/ton) decreased 0.039 (0.15 Kg-Cal/ton)for the test period. However, the energy consumption(MMBtu/ton) decreased 0.117 (0.46 Kg-Cal/ton) untilthe operator started raising the loop pressure. Theresults show that the interrelationships between theammonia plant variables are very complicated.

Other valuable results from the model are:(1) The input variables are listed in a sensitivity

table which shows which variables have the greatestimpact on the production rate and energy savings.This sensitivity analysis is used to decrease the num-

Table 2. Adaptive Technologies Example Dataset

A sensitivity analysis of the 200+ variables for the synthesisgas converter adaptive technologies model was performed todecrease the number of input variables to 65.

Continuous update

73.677Z.S572.28171.6170.8970.2269.5668.8468.1767.5066.83

avrg10_production 0 (Output)

Predicted

Actual

Figure 6. Desired production rate vs. predictedand actual achieved.

ber of variables to produce a more efficient model.(2) Figure 4 shows the accuracy of the predictions.

Figures 4, 5 and 6 are for a second synthesis gas con-verter model that was developed in March 1996.

(3) Figure 5 shows the highly nonlinear relationshipbetween these variables. This is probably why otheradvanced process control methods have not been suc-cessful on a synthesis gas converter.

(4) Figure 6 shows how the model "What Ifs" areused to study the plant operations. In a "What If'analysis, one specifies a value for every input variablein the model, and the adaptive technologies softwarecalculates predicted values for all of the output vari-ables. This resulted in the operations model graph inFigure 6 where the requested production rate was setat a desired rate allowing the software to show theactual production from the plant and to predict a newplant production rate.

To Sum Up

(1) The synthesis gas converter adaptive technolo-gies economic optimization and advanced process

AMMONIA TECHNICAL MANUAL 328 1997

Table 3. Synthesis Gas Converter Adaptive Technologies Model Results

InitialFinalFinal-Initial

* Valve position percent open.

TPD

1,4581,48224

Quench 1%

61.3274.6013.28

Quench 2%

67.9596.4728.52

Quench 3%

98.0091.00-7.00

Quench 4%

98.00107.00

9.00

Table 4. Synthesis Gas Converter DCS Verification of the Results in Table 3

Hours TPD TPH MMBtu/ton H/N RATIO LOOP PRES.

13:0014:0015:0016:0017:0018-0019:00Diff.

1r4421.4461.4481.4511.4491 4551,45210

60.0960.2660.3560.4660.38606160.48

0.39

34.77434.72334.67434.65734.6763470534.735-0.039

2.9502.9482.9462.9482.94729492.9480.002

1r9971.9951.9931.9932,0022r0142,014

17

control model was used to increase the ammonia pro-duction rate 30 TPD (27 metric TPD).

(2) The model was used to decrease the energy con-sumption (MMBTU/ton) of ammonia approximately0.1 MMBtu/ton (0.4 kg»cal/ton).

(3) The open loop control model was only taken off-line four times in a seven month period.

(4) The closed loop model has been in operation forapproximately two months since March 1996.

(5) A synthesis gas converter adaptive technologieseconomic optimization and advanced process controldevelopment model can be completed in less than two

months, if the computer hardware is available toobtain a dataset.

(6) From the time a dataset is obtained, to the time ittakes to complete a synthesis gas converter open anda closed loop control system, is approximately sixmonths. This assumes there are very few plant opera-tional problems that would delay the project.

(7) The adaptive technologies software is easy touse. However, to complete an open loop and closedloop control system in a few months, engineers withseveral years of advanced process control experienceare needed.

AMMONIA TECHNICAL MANUAL 329 1997