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Expert system of a crude oil distillation unit for processoptimization using neural networks

Leo Chau-Kuang Liaua,*, Thomas Chung-Kuang Yangb, Ming-Te Tsaib

a Department of Chemical Engineering, Yuan Ze University, 135 Yuan-Tung Rd, Chungli 320, Taiwan, ROC b Department of Chemical Engineering, National Taipei University of Technology, Taipei 100, Taiwan, ROC 

Abstract

An expert system of crude oil distillation unit (CDU) was developed to carry out the process optimization on maximizing oil production

rate under the required oil product qualities. The expert system was established using the expertise of a practical CDU operating system

provided by a group of experienced engineers. The input operating variables of the CDU system were properties of crude oil and manipulated

variables; while the system output variables were defined as oil product qualities. The knowledge database of the CDU operating model can

be built using the input–output data with an approach of artificial neural networks (ANN). The built ANN model can be applied on predicting

the oil product qualities with respect to the system input variables. In addition, a design of experiment was implemented to analyze the effect

of the system input variables on the oil product qualities. Optimal operating conditions were then found using the knowledge database with an

optimization method according to a defined objective function. The built expert system can provide on-line optimal operating information of 

the CDU process to the operators corresponding to the change of crude oil properties.

q 2003 Elsevier Ltd. All rights reserved.

Keywords:  Expert system; Artificial neural networks; Crude oil distillation; Process optimization; Design of experiment

1. Introduction

A distillation unit is generally and widely utilized in

chemical and petroleum industries for separation of 

mixtures characterized by boiling points. A crude oil

distillation unit (CDU) is one of the critical and important

unit operations for petroleum industry. The operating goal

of a CDU is to achieve well-controlled and stable system,

high production rate and product quality as well as low

operating cost for the economic consideration. Therefore,

the engineering design, control strategy and process

optimization of a CDU has been paid attention to improve

product efficiency and quality assurance in petroleum

industry in recent years (Sea, Oh, & Lee, 2000).

The separation process of a CDU involves many complex

phenomena between input and output operating variables of 

the system, although only physical instead of chemical

reaction presents in the unit. The input variables are usually

crude oil properties and manipulated variables of CDU,

such as energy supply inputs, reflux ratios, and product flow

rates; while the output variables are the oil product qualities,

system operating performance, or the plant profit. In a CDU

operation, the objective is to perform a process optimi-

zation, including high production rate with required product

quality and low operating costs by searching an optimal

operating condition of the operating variables.

However, to search and maintain an optimal operating

condition is complicated because of the non-linear inter-actions between the operating input and output variables. In

addition, the optimal manipulated variables of CDU have to

be frequently adjusted due to the variation of crude oil

properties. Furthermore, if specifications of oil products

cannot be reached or the CDU operation is not stable, the oil

supply can cause some problems in plant management or

even the process needs to be shut down. All these sequences

raise the need to control and optimize the complex CDU

operation. In recent years, the research of CDU process was

focused on the subject of process control and optimization

(Mizoguchi, Martin, & Hrymark, 1995).

Complex processes generally inherit higher non-linearity

and uncertainty of system models. In a petroleum plant,

optimal manipulated variables of a CDU process were

0957-4174/$ - see front matter q 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/S0957-4174(03)00139-8

Expert Systems with Applications 26 (2004) 247–255www.elsevier.com/locate/eswa

*   Corresponding author. Tel.:   þ886-346-388-00x573; fax:   þ886-345-593-73.

E-mail address: lckliau@saturn.yzu.edu.tw (L.C-K. Liau).

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decided and adjusted mostly by experienced operators

according to the input crude oil conditions. The judgment of 

the optimal operating conditions for a CDU process is quiet

sophisticated and relied on the help of the experienced

experts to avoid misjudgments. However, if the experienced

operators are not available all the time for a continuous

CDU operation or the decision-making is not correct, the

product quality specification or even the optimal operation

cannot be reached. The oil product quality cannot be assured

for the CDU operation. Therefore, on-line support for the

optimal operating information to the operators is quite

essential to maintain a proper management of the CDU

operation.

Expert systems, one field of Artificial Intelligence, apply

expertise to provide solutions for many complex systems in

recent years (Giarratano & Riley, 1993). Generally, thebasic structure of an expert system is consisted of a

knowledge database and an inference engine to reason a

proper answer to the domain user. The knowledge database,

a core of the system structure, was built using the collection

and organization of the experts’ experiences in a particu-

larly defined system. Methods such as fuzzy logic, artificial

neural networks (ANN), or neuro-fuzzy, are generally used

to construct the knowledge database. This database can be

represented by a set of rules, a pattern, or even topological

figures, relating the input to the output of the operating

system (Jackson, 1999).

ANN approach has been implemented and found as one

of the effective ways to model complex processes due to the

non-linear characteristic of the ANN structure. The scheme

has been utilized to an optimal control in autoclave curing of 

composites (Joseph & Hanratty, 1993), in semiconductor

fabrications (Card, Sniderman, & Klimasauskas, 1997) and

in a batch polymerization process (Tsen, Jang, Wong, &

Joseph, 1996). In addition, ANN model-based technique for

the process optimization has been adopted successfully on

many systems, such as a chemical reaction system (Keeler,

Hartman, & Piche, 1998), industrial chemical processes

(Nascimento, Giudici, & Guardani, 2000), brightness of a

coated paper system (Kumar & Hand, 2000), and solar cell

processing (Liau et al., 2002).In this work, an expert system of a CDU process was

established for providing optimal operating information for

the system operators in defined optimization problems. The

scheme to construct this expert system is first to build

the knowledge database of the CDU operating system. The

database was built using an ANN approach with the CDU

operating data, provided by the experienced engineers. This

knowledge database, represented and stored by an ANN

configuration was then used to estimate the optimal

operating conditions of the CDU process using an

optimization method. This expert system can be

implemented to predict an optimal operating condition to

the operators for the CDU operation. In addition, the effectsof the operating variables on the product qualities of 

the CDU process were also investigated using a design of 

experiment (DOE) method.

2. CDU process optimization

2.1. CDU process operation

The function of a CDU is to separate the crude oil into

many kinds of petroleum products. A typical CDU diagram

is shown in Fig. 1. In this work, the input variables of the

CDU operation are crude oil properties (uncontrollable) and

the manipulated (controllable) variables, such as feed

temperature of crude oil, product flow ratios; while the

output variables are the distilled oil product qualities out of 

the CDU, including kerosene, diesel, and AGO. The judgment of crude oil quality is from the index values of 

API (American Petroleum Institute) gravity, sulfur content,

and the composition of light petroleum gas, naphtha,

kerosene, diesel, and AGO data provided by the oil supplier.

If any of these input variables are changed or adjusted, each

of the distilled oil product qualities can be affected and

altered due to the non-linear interactions between the input

and output variables. Moreover, this non-linear character-

istic can even influence the control stability of the CDU

tower.

In a practical CDU operating condition, one of the

essential requirements is that all these distilled products

have to be satisfied to the specifications as listed in Table 1.In this table, the check points of the temperatures are

referred to the ASTM D86 curves of the distilled oil

products. For example, the product of kerosene is satisfied

with the quality standard if 100% of the kerosene sample

ðT 100Þ  is distilled all below 300 8C. While   T 90   and   T 10   are

standard temperature data checked for diesel and AGO

quality, respectively. However, the temperature distri-

butions cannot be verified until the independent distillation

tests are carried out. The product quality can be thenevaluated by the temperature data after the analysis of the

distillation test. Therefore, the operating constraint is that

the oil quality cannot be known on-line under any operating

condition of the CDU. The temperature distribution of eachdistilled product cannot be known before hand even if the

CDU tower temperature and pressure distributions are well-

controlled. This is because there are uncertain phenomena

presented inside the CDU system, i.e. heat and mass transfer

phenomena.

The product quality (output variables) can be directly

related to the operating (input) variables if an operating

model known. To establish this model, the input–output

experimental data have to be measured and collected in a

CDU operating system. The system model then can be built

by a modeling approach with these experimental data. The

model can express the relationship between the operating

condition and the product quality. Besides the requirementof each product quality, the price fluctuations of these

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products are much sensitive to their demands in the world’s

oil market. Therefore, the operating strategies are pro-

grammed to maximize the production rate of the high price

product to achieve the highest profits for the plant manage-

ment. Hence, the evaluation of the CDU operatingperformance is based on the production rates with the

required product quality with respect to the CDU operating

variables.

2.2. Process optimization

The optimization problem of the CDU operation can be

stated as

Max   f ð x; m; yÞ

subject to  mlb % m % mub

 y  ¼  U ð x;

mÞgð x; m; yÞ $ 0   ð1Þ

Where   f    is the objective function,   x   is uncontrollable

variables,   m   is controllable variables,   y   is the distilled oil

product quality, U  is the knowledge database (model) of the

CDU operating system, and  g  is the unequality constrain to

satisfy the product specification in this problem. The

notations of subscript lb and ub represent lower bound

Table 1

Standard specification of the distilled oil product

Oil product Standard control of the distillation temperature curve

T 10a

T 90b

T 100  (end point)c

Kerosene – –   , 300 8C

Diesel – 290, 338 8C –

AGO   .280 8C

a

Temperature at 10% of the oil product (AGO) is distilled.b Temperature at 90% of the oil product (diesel) is distilled.c Temperature of the oil product (kerosene) is totally distilled.

Fig. 1. A practical configuration of a CDU.

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and upper bound, respectively. The optimization problem

can be solved using a non-linear constrained optimization

algorithm if the system model   U   is obtained. In this

optimization problem,   U   has to be determined first to

describe the relationship between the input   ð x; mÞ   and the

output ð yÞ variables.

2.3. ANN approach

The goal of ANN approach is to construct the

operating model (knowledge database) of the CDU

operating, concerning about the operating input and

output relationships. The advantage of using ANN

approach is that fewer experimental data are required

to construct the database to represent the CDU operating

model compared with other techniques. Besides, ANNmodeling approaches are more efficient and accurate for

the systems inherited non-linear interactions among

several variables.

The ANN modeling approach adopts sets of input–

output experimental data to train a defined network. The

structure of the ANN used in this work is a multi-layer

forward network with hidden layers shown in   Fig. 2. The

common training method is an error back-propagation

training algorithm. In this training algorithm, the error is

defined as

errorðnÞ ¼1

2

Xm

 j¼1

ðd  jðnÞ 2 y jðnÞÞ ð2Þ

where   d  j   represents the practical data of the   jth output

neuron,   y j   is the computed data of the   jth output neuron

obtained from the basic ANN calculation method,  m is the

neuron number,   n   is the training step. The error is back 

propagated through the network to adjust the weights of 

the connecting links in the ANN structure during the

training period. The training is incomplete until the error

is sufficiently small. Detailed descriptions of the ANN

algorithm can be found elsewhere (Heykin, 1999).

3. Method

The expert system of the CDU for process optimization

was carried out in two phases. The first phase was to

establish knowledge database of the CDU operation by a

group of experienced experts. In the second phase, the

process optimization was reasoned using the knowledge

database with an optimization approach to search for an

optimal operating condition of the CDU. The built expert

system hence consisted of the knowledge database and the

reasoning ability of finding an optimal condition.

The knowledge database was constructed by the ANN

modeling approach to describe the relationship between the

input and output variables of the CDU. The input data are

the uncontrollable (crude oil properties) and controllable

(manipulated parameters) variables; while the output dataare the temperature distributions as defined in Section 2.1.

The analytical data were measured and collected in a real

petroleum plant operated by a group of experienced

engineers during a six-month period. Moreover, all these

experimental data used to establish the knowledge database

of the CDU operation were satisfied to all the product

quality. Total 212 sets of the input–output data were used in

the ANN modeling for the CDU operation. An ANN toolbox

developed by MATLAB (The MATH WORKS Inc.) was

utilized for the ANN modeling. The ANN model can be

built after appropriate training and testing. This built ANN

model can represent the knowledge database of the CDU

operation; hence, the expertise was able to describe theCDU operation.

The process optimization can be carried out to find the

optimal operating solution of the CDU operating problem

after the knowledge database was obtained. The optimiz-ation toolbox provided by the MATLAB (The MATH

WORKS Inc.) was adopted to solve the non-linear

constrained optimization problem as described in Eq. (1).

The optimum solution is able to offer the operators to set up

the optimum parameters of the CDU operation regarding to

the supply of variant crude oil properties.

The built expert system helps the operators not only

making but checking the decisions of the process optimi-

zation condition. In addition, the expert system can beimplemented to help the inexperienced engineers being

familiar with the decision-making procedures toward the

search of the optimal operating conditions.

4. Results and discussion

4.1. ANN modeling

The ANN model was trained to represent the knowledge

database of the CDU operating system. The ANN was

trained using the experimental data collected during a six-

month operating period. The trained ANN model was thentested with some data selected randomly after the six-monthFig. 2. A feed-forward structure of artificial neural networks.

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operating period. The testing results of the output data are

analyzed and demonstrated in  Table 2.  The relative errors

between the experimental data measured from the CDU

operation and the computed data calculated using the ANN

model are below 8% demonstrated in this table. Therefore,

the ANN model is quite reliable to describe the CDU input–

output relations after the training.

The relationship between input and output operating

variables can be correlated using the built ANN model.

Figs. 3– 5   shows the correlations of the controllable

valuables with   T 90   of diesel,   T 10   of AGO, and   T 100   of 

kerosene predicted by the ANN model.   Fig. 2   shows the

distribution of   T 90   data related to the feed temperature of 

crude oil, and diesel and AGO flow ratios. The   T 90   data

rose in a low AGO flow ratio as diesel flow ratio increased.

On the contrary, the minimum   T 90   region appears at high

feed temperature and a low diesel flow ratio but a high

AGO flow ratio. The product quality specification of the

diesel   T 90   was confined between 290 and 338 8C.

However, the maximum   T 90  region locates around higher

diesel flow ratio and the feed temperature but lower AGO

flow ratio. It indicates that these operating parameters can

be adjusted to satisfy the diesel specification for the CDU

operation.

Fig. 4  elucidates the influence of feed temperature and

diesel as well as kerosene flow ratios on  T 10 values. T 10 data

appear in the higher values if the operation is under high

feed temperature and diesel flow ratios. However,  T 10 value

is constrained to be greater than 280 8C for the specification

of the AGO quality. The distribution of   T 10   near

Table 2

Testing results of the ANN model

T 100  of kerosene (8C)   T 90  of Diesel (8C)   T 10  of AGO (8C)

Test no. Compa Expb Err (%)c CP Exp Err (%) Comp Exp Err (%)

1 248.3 253.7 2 316.6 333.1 5 311.9 310.4 0

2 249.7 257.7 3 326.7 324.2 1 309.3 321.7 4

3 256.1 252.5 1 323.7 319.4 1 326.4 315.6 3

4 254.9 263.9 3 302.3 320.2 6 304.8 328.7 7

5 258.8 261.1 1 313.5 307.4 2 326.1 309.1 5

6 260.3 258.5 1 315.9 323.2 2 310.3 309.8 0

7 250.9 245 2 321.4 325.5 1 327.8 302.6 8

8 247.7 248.3 0 319.4 312.6 2 308.4 308.6 0

9 249.7 251.9 1 312.7 314.1 0 320.5 311.1 3

10 248 250 1 332.2 334.1 1 322 329.6 2

11 236.8 238.4 1 344.8 332.4 4 306.6 317.9 4

12 248.9 245 2 311.1 316.9 2 297.1 290.2 2

a Computational data.b Experimental data.c Relative error.

Fig. 3.  T 90  as functions of feed temperature, diesel and AGO flow ratios.   Fig. 4. T 10  as functions of feed temperature, diesel and kerosene flow ratios.

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the constrained temperature is located around the corner of 

lower diesel but higher kerosene flow ratios shown in  Fig. 4.

Fig. 5   depicts the   T 100   values are affected by the feed

temperature and kerosene and AGO flow ratios. Although

T 100  data all fits the kerosene product specification (below

300 8C), one still can observe that the distribution of   T 100

was influenced by these manipulated variables.

4.2. Design of Experiment (DOE)

The DOE method was applied to evaluate the effect of 

each input variable on the product quality of the CDU

process. Factors selected in this study are crude oil

properties (uncontrollable variables) and manipulated (con-

trollable) variables. A Latin square table (L27) shown in

Table 3 was design to carry out the analysis. There are three

levels selected within the operating ranges for each factor.

The output data, T 100; T 10; and  T 90; were predicted using the

ANN model for each design operating condition listed in

Table 2.

The effect of each input variable on the output variablesis estimated using a statistical method shown in Table 3. The

equation used to calculate the influential degree is written as

T ij   ¼X

i

T ij

mð3Þ

where T  is the output data,  T  is the mean of the output data,

subscript i  is  ith experimental runs and j  represents an input

factor, and m is the total number of the experimental runs

only for the input factor   j:  For example, the effect of level

1of A (API gravity) on   T 100   is computed by averaging

Fig. 5. T 100 as functions of feed temperature, AGO and kerosene flow ratios.

Table 3A Latin square table (L27(37)) for the DOE method

No. A B C D E F G   T 100   T 90   T 10

1 1 1 1 1 1 1 1 245.9 316.0 291.3

2 1 1 1 1 2 2 2 245.6 300.4 270.8

3 1 1 1 1 3 3 3 247.7 322.0 277.8

4 1 2 2 2 1 1 1 250.2 294.2 292.1

5 1 2 2 2 2 2 2 244.4 294.0 260.9

6 1 2 2 2 3 3 3 250.6 311.6 268.0

7 1 3 3 3 1 1 1 224.3 448.2 483.6

8 1 3 3 3 2 2 2 238.1 353.7 422.6

9 1 3 3 3 3 3 3 229.3 383.8 344.8

10 2 1 2 3 1 2 3 253.2 333.3 340.2

11 2 1 2 3 2 3 1 248.3 324.7 329.6

12 2 1 2 3 3 1 2 257.2 332.9 334.4

13 2 2 3 1 1 2 3 239.7 343.2 351.5

14 2 2 3 1 2 3 1 239.1 360.9 398.5

15 2 2 3 1 3 1 2 231.1 383.2 366.2

16 2 3 1 2 1 2 3 249.2 326.2 301.3

17 2 3 1 2 2 3 1 248.0 316.9 311.8

18 2 3 1 2 3 1 2 247.4 328.9 308.6

19 3 1 3 2 1 3 2 237.2 337.2 331.1

20 3 1 3 2 2 1 3 239.9 333.8 321.1

21 3 1 3 2 3 2 1 244.0 320.7 322.1

22 3 2 1 3 1 3 2 255.1 359.9 344.1

23 3 2 1 3 2 1 3 259.1 406.3 438.8

24 3 2 1 3 3 2 1 250.9 327.2 341.6

25 3 3 2 1 1 3 2 249.7 351.8 358.3

26 3 3 2 1 2 1 3 254.7 407.7 419.8

27 3 3 2 1 3 2 1 245.0 328.3 339.9

A, API gravity; B, Sulfur (wt%); C, Crude oil composition; D, Feed temperature; E, Kerosene flow ratio; F, Diesel flow ratio; G, AGO flow ratio.

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the   T 100   data from test no. 1–9 in   Table 3   regardless the

change of other factors.   Fig. 6a–c   are the results of the

factor analysis for the output temperatures, respectively. In

Fig. 6a, T 100 is more significantly influenced by the crude oil

properties than the manipulated variables. In the other hand,

Fig. 6b and c elucidate that T 90 and  T 10 are most affected by

the feed temperature; while the kerosene flow ratio appears

the least influential factor for  T 90  and factor of AGO flow

ratio for   T 10:   Therefore, results of DOE imply the

effectiveness of each operating factor on the productqualities in the CDU operation.

4.3. CDU process optimization

In this study, the objective function is to maximize one of 

the oil production rates according to the market needs. The

boundedof themanipulatedvariables aredefined as following

0:15 % Kerosene flow ratio % 0:25

0:25 % Diesel flow ratio % 0:35

0:

03%

AGO flow ratio%

0:

10334 % Feed temperature % 344   ð4Þ

Fig. 6. The effect of the input variables on (a)  T 100  (b)  T 90  (c) T10 using DOE method.

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Constrains of the oil product are specified as shown in

Table 1.   The optimization problem stated in Eq. (1) was

solved using a process optimization method with the built

ANN model. The objective function of the optimization

problem was defined and discussed in the following cases.

Case 1. The objective function is defined to maximize a

kerosene flow ratio with respect to the operating variables for

this case. The maximum kerosene flow ratio was determined

at its upper bound (0.25) no matter what the crude oil

properties were varied. Fig. 7 illuminates the contour of the

kerosene flow ratio at 0.25 corresponding to different API

gravityandsulfur contentwith adjusting thefeedtemperature.

The curve indicates that if the optimum condition has to be

reached, the operating condition must be adjusted by tuning

the feed temperature according to different crude oil proper-

ties. For instance, if the crude oil has higher API gravity (38)and sulfur content (25%), then a feed temperature has to be

setup at 337 8C to maintain themaximumkerosene flowratio.

Case 2. In this case, the objective function was defined to

maximize a diesel flow ratio regarding to the operating

variables. The optimum solution of the maximum diesel

flow ratio is predicted at its upper bound (0.35) for the

operating conditions of different crude oil properties. The

contour of the flow ratio at 0.35 corresponding to different

operating variables is shown in   Fig. 8. The optimal

operating operations were dependant on the feed tempera-

ture. This contour indicates that higher feed temperature is

needed with lower API gravity and higher sulfur content

values of crude oil. The optimal feed temperature is locatedaround 340 8C for higher API gravity with any sulfur

content in case the diesel flow ratio is maximized.

Case 3. In the next case, the objective function is to

maximize the AGO flow ratio associated with the operating

variables. The optimal operating conditions were searched

andpredicted at the upper bound (0.10)of the AGOflow ratio

for different conditionsof crudeoil properties. The contour of 

the maximum flow ratio corresponding to different system

variables is shown in  Fig. 9. This figure shows that higher

feed temperature is needed around the bounded API gravity

values; while theloweroptimal feed temperature is located in

the middle of the API gravity value.

The expert system can be used to optimize the CDU

operation according to the defined objective functions. The

optimization problems of the CDU were solved using the

knowledge database consisted of the expertise. However, if 

the database is not sufficient enough to handle special cases,

then the answer reasoned from the expert system can

possibly be incorrect. For example, the database does

not contain the expertise of CDU operating of higher sulfurwt% of crude oil than 30%. The optimal operating data

predicted from the expert system can present an error for

this case. Therefore, the knowledge database has to be

updated to improve the problem-solving ability of the expert

system.

The built expert system can predict and indicate the

effects of the operating variables on the product qualities.

For instance, the feed temperature is one of the important

parameters to affect the product flow ratios and the product

Fig. 7. The Contour of maximum kerosene flow ratio (0.25) with respect to

the operating variables.

Fig. 8. The Contour of maximum diesel flow ratio (0.35) with respect to the

operating variables.

Fig. 9. The Contour of maximum diesel flow ratio (0.10) with respect to the

operating variables.

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qualities. Moreover, the expertise of the CDU operation is

valuable knowledge for the inexperienced operators to train

and be familiar with the CDU system. In addition, the expert

system can reason optimal solutions of the defined

optimization problems from the expertise database.

5. Conclusions

The expert system of the CDU operation was founded to

predictthe optimaloperatingconditionsfor different objective

functions.The knowledge database waswell establishedusing

ANN modeling scheme with expertise of the CDU operators.

The ANN model can represent and describe the CDU process

for the input (system operation) and output (product quality)

relations. The manipulated variables affected the productqualities were evaluated by the DOE method. Practical

optimization cases of maximizing the oil production rates can

be estimated by solving the constrained optimization problem

using the expert system. The expert system can provide the

optimal operating conditions of the CDU to the process

engineers regarding to the operating variables. In addition, the

expert system can offer the inexperienced operators for the

training and decision-making to find the optimum operating

conditions of the CDU operation.

Acknowledgements

This work is partially supported by the National Science

Council, Taiwan, ROC under grant NSC 91-2214-E-155-

009. The financial support is gratefully acknowledged.

References

Card, J. P., Sniderman, D. L., & Klimasauskas, C. (1997). Dynamic neural

control for a plasma etch process.   IEEE Transaction on Neural

 Networks,  8, 883–901.

Giarratano, J., & Riley, G. (1993).   Expert systems: principal and 

 programming. Boston, MA: PWS Publishing.

Heykin, S. (1999).   Neural networks. Upper Saddle River, New Jersy:

Prentice-Hall.

Jackson, P. (1999).   Introduction to expert systems. England: Addison

Wesley Longman.

Joseph, B., & Hanratty, F. W. (1993). Predictive control of quality in a

batch manufacturing process using artificial neural network models.

 Industry Engineering and Chemistry Research,  32, 1951–1961.

Keeler, J. D., Hartman, E., & Piche, S. (1998). Process modeling and

optimization using focused attention neural networks.   ISA Trans-

actions,  37 , 41–52.

Kumar, A., & Hand, V. C. (2000). Feasibility of using neural networks and

genetic algorithms to predict and optimize coated paper and boardbrightness.   Industry and Engineering Chemistry Research,   39,

4956–4962.

Liau, L. C.-K., Huang, C.-J., Chen, C.-C., Huang, C.-S., Chen, C.-T., Lin,

S.-C., & Kuo, L.-C. (2002). Process modeling and optimization of 

PECVD silicon nitride coated solar cell using neural networks.  Solar 

Energy Material and Solar Cells,  71, 169–179.

Mizoguchi, A., Martin, T. E., & Hrymak, A. N. (1995). Operations

optimization and control design for a petroleum distillation process. The

Canadian Journal of Chemical Engineering,  73, 896–907.

Nascimento, C. A. O., Giudici, R., & Guardani, R. (2000). Neural network 

based approach for optimization of industrial chemical processes.

Computer and Chemical Engineering,  24, 2303–2314.

Sea, J. W., Oh, M., & Lee, T. H. (2000). Design optimization of 

crude oil distillation.   Chemical Engineering Technology,   23(2),

157–164.

Tsen, A. Y-D., Jang, S. S., Wong, D. S. H., & Joseph, B. (1996). Predictive

control of quality in batch polymerization using hybrid ANN models.

The Journal of American Institute Chemical Engineering,   42,

455–465.

 L.C.-K. Liau et al. / Expert Systems with Applications 26 (2004) 247–255   255