cdu-opt
TRANSCRIPT
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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: [email protected] (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.
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