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Neural Network Models Used for Quality Prediction and Control
Copyright © IFAC Artificial Intelligence in Real-Time Control, Arizona, USA, 1998
NEURAL NE1WORK MODELS USED FOR QUALIIT PREDICTION AND CONTROL
Mika Jarvensivu and Brian Seaworth mika.j arvensi vu @hut.fi Helsinki University of Technology Process Control Laboratory 02150 Espoo, Finland [email protected] Gensym Corporation 125 Cambridge Park Drive Cambridge, MA 02138
Abstract: The objective of this paper is to illustrate the applicability of neural networks (NNs) for predicting the behavior of an industrial nonlinear and multi variable process. This is demonstrated using industrial application of neural network predictor for burned lime residual CaC03 content. The neural network predictor is deployed as part of the real-time closed-loop control system for a lime mud reburning process. The presented work was carried out as a co-operative effort between academic and industrial representatives, and the developed system was implemented at the Pietarsaari pulp mill in western Finland. Copyright ©19981FAC Keywords : Neural networks, Backpropagation, Optimal Control, Soft sensing, Expert Systems
phenomena involved in the process, and always a certain degree of uncertainty in the actual process measurements. It is therefore difficult to obtain a good phenomenological model for a complex nonlinear industrial process.
1. INTRODUCTION During the last decade, Advanced Process Control (APC) strategies and optimization techniques have been extensively applied in many industrial processes. Although there are many different ways of implementing APC, the common prerequisite for the success of each implementation principle is the availability of reliable, fast, accurate measurements and/or predictions (Deshpande, 1996; Friedmann, 1995; Anderson 1994; Boullard, 1992).
Another alternative is an empirical modeling approach, in which the models are obtained exclusively from the available process data. These may include measurements collected from normal operations and/or during process experiments, e.g. step tests. Empirical models can be based on models with definite structure or on models with an undefined structure, i.e. black-box models . However, knowing the required input-output structure of the model beforehand may be problematic, especially if the process involved is highly complex and there are
Obtaining an accurate and not too oversimplified phenomenological model to explain the underlying physics of an industrial process is a demanding and time-consuming task. In addition, there is often a lack of sufficient understanding of the exact physical
179
significant nonlinearities. Therefore, the unstructured black-box approach, e.g. neural networks can be more appropriate (Sjoberg, 1995 ; Pradeep, 1997; Ramasamy, 1995 ; Brambilla, 1996; Willis, 1992; Song, 1993; Hunt, 1992).
with visual observations and the prior theoretical and experimental knowledge of the lime kiln process behavior to determine the most relevant process measurement for the quality prediction. The past values of the selected process measurement as shown in the Table I and the results of the corresponding laboratory analysis were used for developing neural network predictor for the residual CaC03 content of the produced lime.
2. DEVELOPMENT OF NNs QUALITY PREDICTOR
2.1 Aim of the presented work Table 1. Input variables used in the model for predicting the residual CaCO} content of the lime
Since 1996, the lime kiln process operation and environmental perfonnance has been extensively studied at the Wisaforest pulp mill. The aim of the studies has been to gather new and more detailed knowledge about the kiln process and furthennore , based on the gathered knowledge to design and develop an intelligent control system for the lime kiln (Jarvensivu, 1998a; Jarvensivu, 1998b).
Residual CaCO:! content
Product quality, which is determined by the residual CaC03 content of the lime, is not measure on-line. Produced lime is manually sampled with an 8-hour time interval. The residual CaC03 content of the sample is analyzed with a delay time of about I to 2 hours in the laboratory. The laboratory analysis is significantly delayed and therefore impracticable from the control point of view and useful only for the monitoring of the kiln operation and product quality.
I
Cold end temperature with 2 hour time delay
2
Middle kiln temperature with I hour time delay
3
Hot end temperature with I hour time delay
4
Heavy fuel feed rate with I hour time delay
5
Production rate with 3 hour time delay_
Data filtering and scaling. After data preprocessing and input variable selection so-called novelty filters were used to remove redundant data pairs and to make sure that the dataset spreads out and evenly covers the input space. In addition, all the input variables were nonnalized from 0 to 1. These nonnalized input variables were then used to generate the input patterns for a backpropagation network
2.2 NN model developed on the basis of historical data Collection of the process data. The lime kiln process was studied by means of a IS-month field survey, which was comprised of both nonnal operations and process experiments. During the field survey, a large amount of the process data at 10-minute and one hour average intervals were collected and archived into the database. The collected data include manual samples analyzed in the laboratory, measurements of the online analyzers and all corresponding process measurements connected to the automation system.
(BNP).
Network training and validation. Next, various types of network structure and the training algorithms were tested to select the appropriate network architecture for the model. Figure I shows an example of the application used to filter and scale the data, as well as to select appropriate network architecture, i.e. train and validate different networks.
Data preprocessing and input variable selection. First the large amount of the raw process data, i.e. about 11000 one hour average values of over 60 measured and calculated variables, were transferred in a more suitable fonn to be pre-processed and analyzed. In the pre-processing, the data were examined visually, i.e. by trend charts and histograms as well as by the statistical methods in order to reject statistical outliers, erroneous process measurements and the data that were gathered during the kiln shutdowns and startups.
The sequence of training with randomly selected sets of data, validation and re-randomization of weights was repeated 10 times for each architecture to achieve statistical representativeness of the results. Figure 2 shows the mean of the training and validation error with the different network architectures. This figure demonstrates clearly that the network with 5 hidden nodes is not sufficiently complex and the network with IS hidden nodes is
After data pre-processing, comprehensive statistical analysis of the data were perfonned. The obtained results from statistical analysis were used together
180
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operational data, but neural networks are not capable of extrapolation. They will give unreliable results when presented with new data that are not represented by the training data. Hence, despite the good results shown above, the use of the quality prediction is used as a part of the closed-loop control system sets special requirements on the reliability. The system needed to be developed further to determine when the predictor starts to extrapolate.
Help
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Automatic detection of the extrapolation is based on another type of network, the radial basis function network (RBFN) (Moody, 1989; Leonard, 1992), trained with the same data as the BPN networks used in actual quality prediction. Cases that require extrapolation are automatically detected to avoid inaccurate predictions.
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Figure 1. An example of data filtering, scaling and selecting an appropriate network architecture
3.1 Intelligent system with embedded neural networks r-------~·------·--- ---------- --
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Overall structure of the control system. The neural network predictor is one part of the intelligent system, . which is connected to the process through the basic automation system and plant wide information system. The plant wide information system is used to collect process measurements from the basic automation system. This is then used to calculate the average values and to archive data into the real time database (RTDB). The intelligent system is connected to the RTDB, providing access to all data, both the current values and history values as well as write-access to the regulatory control loops in the basic automation system The overall structure of the implemented system is shown in the Figure 4.
NNs ARCHITECTURE SElECTION 0.750 0.650 FITTING ERROR 0.550 0.450 0.350
t....... i...J.i...J.i;ii.lr' 5 10 15 8 NUMBER OF HIDDEN NODES
Figure 2. Fitting errors with different architectures
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Figure 3. Fitting results with 8 hidden nodes Figure 4. Overall structure of the intelligent system
overly complex for the problem at hand. Finally, the best architecture with one hidden layer and 8 hidden nodes was trained using all the available data. A comparison of the laboratory analysis and the neural network outputs is shown in Figure 3.
The Main functions of the intelligent system. intelligent control system performs high-level control functions to stabilize and optimize the 1ciln operation. The application includes the following main functions as shown also in Figure 5: (Jarvensivu and Ruunsunen, 1998)
Detection of the extrapolation. Neural networks have the ability to learn and adapt behavior of past
181
• •
• • •
basic engineering calculations, neural network models based feedforward control of the kiln rotation, draft fan speed and fuel flow rate, neural network based calculation of the target value for the temperature and excess oxygen, neural network based prediction of the residual CaC03 content, high level feedback corrections based on the fuzzy logic principles and the natural language rules
FAN.RPM. NNO + se + QC NNO-IC_ _ 'aI,,*,,).
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Figure 6. Principle of the feedforward control Table 3. Examples of the rules used to determined feedback corrections Rules for hi h level corrections if CET high and HET low and 02 low then decrease FAN s
Draft fan Speed
Figure 5. Main functions of the intelligent system
Kiln Rotation
Feedforward control. Feedforward control of the kiln rotation, draft fan speed, and fuel flow rate are based on the neural network models which were developed using the same pre-processed data as the quality prediction. The models are used for changing the set point of the regulatory control loops during the production rate changes. Furthermore, the neural network models are used to determine bounds for the acceptable set point at the specific production rate. The principle of the feed forward control is show in Figure 6.
Fuel flow rate
•
if Kll.N load high then increase Kll.N
•
if MKT (high+increas.) and HET (high+increas.) then decrease FUEL flow rapidly if MKT (ok+increas.) and HET (Iow+increas.) then wait
•
CET = cold end temperature. MKT = middle kiln Temperature. HET = hot end temperature
High level adjustments. High level adjustments are functions designed to maximize the production capacity and energy efficiency of the kiln, and to optullize product quality and environmental performance.
Target value calculation and feedback corrections. Neural network models are also used to calculate target values for the kiln temperature profile, and the excess oxygen content of the flue gas. The input variables used for the models are delayed production rate and heavy fuel oil flow.
The throughput maximization module determines the kiln production rate. The main function of the module is to determine the maximum production rate the kiln can sustain under current conditions. The maximum sustainable production rate is determined based on the prediction of the kiln load. The kiln load is predicted with neural networks by using the draft in the feed end of the kiln and the power required for rotating the kiln. In addition, the module determines the required kiln production rate based on the current value and trend of the lime mud storage level.
Feedback corrections are based on the calculated target values, and the current process measurement and the change over the specified time period. The fuzzy logic principles and/or natural language type of rules determines required corrections. Figtrre 6 shows also how the feedback corrections are combined together with the feed forward part of the system. Some examples of the rules determining the required feedback correction are shown in Table 3.
The minimization of the energy consumption and environmental protection are taken into account in the same module. The module adjusts the target value for excess oxygen content and wash water of the lime mud filters based on the total reduced sulfur (TRS) emission level.
182
The target value for excess oxygen is decreased with small steps to achieve improved energy efficiency (i.e. less cold air needed to be warmed up) if TRS emission level is low. In contrast, the target value for excess oxygen is increased to avoid environmental impact if the TRS emission level starts to increase. Similarly, wash water to the lime mud filters is reduced to achieve low moisture content of the lime mud fed into the kiln, if TRS emission is low. The amount of the wash water is increased to achieve better washing, if TRS emission level starts to increase.
The upper chart shows all data collected during the first month in operation. The lower focuses on the highlighted portion of the data. As the top portion shows, extrapolation was detected six times during the examined period. Overall, the developed neural network model is predicting the product quality fairl y well. RESIDUAL CAC03 PREDICTlON 7.5 e.5
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Product quality optimization.
The optnruzation module of the product quality (residual Cac~ content of the reburned lime), schematically represented in Figure 7, minimizes variations by maintaining the middle kiln and burning end temperature at the desired target values .
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Figure 7 . Product quality optimization principle Figure 8. Quality predictions and laboratory analysis
The product quality is maintained at the optimum level between 2 and 3 % by making adjustments for the middle kiln and hot end temperature targets. In addition to the laboratory analysis presented neural network prediction of the residual Cac~ content is used when the required adjustments are determined.
Figure 9 shows that under manual control the mean value of the excess oxygen was about 4 % whereas automatic control averaged around 2.5% excess oxygen. Reduction of the excess oxygen makes the kiln more energy efficient and decreases draft in the kiln. This consequentially reduces dust circulation in
3.2 Results The presented neural network predictor has been in use at the Pietarsaari pulp mill in Finland since March 1998 as part of the intelligent real-time control system, which has been in operation since April 1998. The neural network prediction (BPN) and laboratory analysis of the residual Cac~ content of the reburned lime are shown in Figure 8. Maximum hidden node activation of the RBFN is also shown in the same Figure. Maximum hidden node activation indicates the confidence of the prediction and can therefore be used for extrapolation detection.
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Figure 9 . Excess oxygen variation. The excess oxygen content of the flue gas in automatic (upper) and in manual (lower) control.
183
5. ACKNOWLEDGEMENTS This work was supported by UPM-Kymmene and the Academy of Finland. The authors wish to thank the staff of the Pietarsaari pulp mill for their valuable support and assistance during the field survey, as well as in the development and implementation stage of the intelligent control system
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Anderson 1. (1994). Getting the most from advanced process control, Chemical Engineering, March 1994, p. 78-89. Boullard, L. (1992). Application of Artificial Intelligence in Process Control, p. 455. Pergamon Press. Brambilla, A (1996). Estimate product quality with ANNs, Hydrocarbon Processing, September, 1996, p.61-66. Deshpande, P. (1996). Improve control with software monitoring technologies, Hydrocarbon Processing, September 1996, p. 81-88. Friedmann, P. (1995). Economics of control improvement, Instrument Society ofAmerica, p. 162. Hunt, K. (1992) Neural networks for control systems, Automatica, 28:6, p. 1083-1112. Jiirvensivu M. (1998a). Evaluation of various alternatives to reduce TRS emission at the lime kiln, TAPPI International Chemical recovery Conference, Tampa, FL, USA Jiirvensivu M. (1998b). Empirical lime kiln process modeling with neural networks, EUROSIM' 98 Congress, April 14-15, 1998, Espoo, Finland. Jiirvensivu M . and P. Ruusunen (1998) Intelligent lime-kiln control system, GUS'98 (Gensym Users Society Meeting), 13-15 May 1998, Newport, RI, USA Leonard, 1. (1992) Using radial basis function to approximate a function and its error bounds, IEEE Trans. Neural Network, 3:4, p. 624-627. Moody, 1. (1989). Fast learning in networks of locally-tuned processing units, Neural Computing, 1989:1, p.281-294. Pradeep, B. (1997) Predict difficult-ta-measure properties with neural analyzer, Control Engineering, July 1997, p. 55-56. Ramasamy, S. (1995). Consider neural networks for process identification, Hydrocarbon processing, June 1995, p.59-62. Sjoberg, J. (1995) Nonlinear black-box modeling in system identification, Automatica, 31:12, p. 16911724. Song J. (1993). Neural model predictive control for nonlinear chemical processes, Journal of Chemical Engineering of Japan, 268 :4, p . 347-354. Willis, M. (1992). Artificial neural networks in process estimation and control, Automatica, 28:6, p. 11811187.
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REFERENCES
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Figure 10. Variation of specific energy consumption in automatic (upper) and in manual (lower) control Also one-hour average values of the specific energy consumption and the actual and target value of the hot end temperature as a function of time. the feed-end of the kiln, which in turn increase the attainable maximum production capacity. Figure 9 presents also one-hour average values of the excess oxygen content and draft fan speed as a function of the time. The excess oxygen was maintained above the critical environmental level of 1.5%. As shown in Figure 10, manual control maintains the specific energy consumption between 4 .6 and 7.0 GJ/tcao. On automatic control it was 5.4 and 6.8 GJ/tcaO' The variation of the specific energy consumption when the intelligent system was in use was reduced over 30% compared to the manual mode. Reduction of the variation makes the kiln process more stabile and it will also significantly reduce lime quality variation. 4. CONCLUSIONS This paper has illustrated the ability of neural network technologies to model a multivariate nonlinear process. The neural network model predicted the quality sufficiently accurately for control. Furthermore the paper presents developed intelligent real-time control system with embedded neural networks, which has been successfully implemented at a Finnish pulp mill. Experience has shown that the experimental knowledge of the process behavior together with proper software tools can be utilized to improve process efficiency and produce considerable benefits both the economical and ecological respect.
184
NEURAL NE1WORK MODELS USED FOR QUALIIT PREDICTION AND CONTROL
Mika Jarvensivu and Brian Seaworth mika.j arvensi vu @hut.fi Helsinki University of Technology Process Control Laboratory 02150 Espoo, Finland [email protected] Gensym Corporation 125 Cambridge Park Drive Cambridge, MA 02138
Abstract: The objective of this paper is to illustrate the applicability of neural networks (NNs) for predicting the behavior of an industrial nonlinear and multi variable process. This is demonstrated using industrial application of neural network predictor for burned lime residual CaC03 content. The neural network predictor is deployed as part of the real-time closed-loop control system for a lime mud reburning process. The presented work was carried out as a co-operative effort between academic and industrial representatives, and the developed system was implemented at the Pietarsaari pulp mill in western Finland. Copyright ©19981FAC Keywords : Neural networks, Backpropagation, Optimal Control, Soft sensing, Expert Systems
phenomena involved in the process, and always a certain degree of uncertainty in the actual process measurements. It is therefore difficult to obtain a good phenomenological model for a complex nonlinear industrial process.
1. INTRODUCTION During the last decade, Advanced Process Control (APC) strategies and optimization techniques have been extensively applied in many industrial processes. Although there are many different ways of implementing APC, the common prerequisite for the success of each implementation principle is the availability of reliable, fast, accurate measurements and/or predictions (Deshpande, 1996; Friedmann, 1995; Anderson 1994; Boullard, 1992).
Another alternative is an empirical modeling approach, in which the models are obtained exclusively from the available process data. These may include measurements collected from normal operations and/or during process experiments, e.g. step tests. Empirical models can be based on models with definite structure or on models with an undefined structure, i.e. black-box models . However, knowing the required input-output structure of the model beforehand may be problematic, especially if the process involved is highly complex and there are
Obtaining an accurate and not too oversimplified phenomenological model to explain the underlying physics of an industrial process is a demanding and time-consuming task. In addition, there is often a lack of sufficient understanding of the exact physical
179
significant nonlinearities. Therefore, the unstructured black-box approach, e.g. neural networks can be more appropriate (Sjoberg, 1995 ; Pradeep, 1997; Ramasamy, 1995 ; Brambilla, 1996; Willis, 1992; Song, 1993; Hunt, 1992).
with visual observations and the prior theoretical and experimental knowledge of the lime kiln process behavior to determine the most relevant process measurement for the quality prediction. The past values of the selected process measurement as shown in the Table I and the results of the corresponding laboratory analysis were used for developing neural network predictor for the residual CaC03 content of the produced lime.
2. DEVELOPMENT OF NNs QUALITY PREDICTOR
2.1 Aim of the presented work Table 1. Input variables used in the model for predicting the residual CaCO} content of the lime
Since 1996, the lime kiln process operation and environmental perfonnance has been extensively studied at the Wisaforest pulp mill. The aim of the studies has been to gather new and more detailed knowledge about the kiln process and furthennore , based on the gathered knowledge to design and develop an intelligent control system for the lime kiln (Jarvensivu, 1998a; Jarvensivu, 1998b).
Residual CaCO:! content
Product quality, which is determined by the residual CaC03 content of the lime, is not measure on-line. Produced lime is manually sampled with an 8-hour time interval. The residual CaC03 content of the sample is analyzed with a delay time of about I to 2 hours in the laboratory. The laboratory analysis is significantly delayed and therefore impracticable from the control point of view and useful only for the monitoring of the kiln operation and product quality.
I
Cold end temperature with 2 hour time delay
2
Middle kiln temperature with I hour time delay
3
Hot end temperature with I hour time delay
4
Heavy fuel feed rate with I hour time delay
5
Production rate with 3 hour time delay_
Data filtering and scaling. After data preprocessing and input variable selection so-called novelty filters were used to remove redundant data pairs and to make sure that the dataset spreads out and evenly covers the input space. In addition, all the input variables were nonnalized from 0 to 1. These nonnalized input variables were then used to generate the input patterns for a backpropagation network
2.2 NN model developed on the basis of historical data Collection of the process data. The lime kiln process was studied by means of a IS-month field survey, which was comprised of both nonnal operations and process experiments. During the field survey, a large amount of the process data at 10-minute and one hour average intervals were collected and archived into the database. The collected data include manual samples analyzed in the laboratory, measurements of the online analyzers and all corresponding process measurements connected to the automation system.
(BNP).
Network training and validation. Next, various types of network structure and the training algorithms were tested to select the appropriate network architecture for the model. Figure I shows an example of the application used to filter and scale the data, as well as to select appropriate network architecture, i.e. train and validate different networks.
Data preprocessing and input variable selection. First the large amount of the raw process data, i.e. about 11000 one hour average values of over 60 measured and calculated variables, were transferred in a more suitable fonn to be pre-processed and analyzed. In the pre-processing, the data were examined visually, i.e. by trend charts and histograms as well as by the statistical methods in order to reject statistical outliers, erroneous process measurements and the data that were gathered during the kiln shutdowns and startups.
The sequence of training with randomly selected sets of data, validation and re-randomization of weights was repeated 10 times for each architecture to achieve statistical representativeness of the results. Figure 2 shows the mean of the training and validation error with the different network architectures. This figure demonstrates clearly that the network with 5 hidden nodes is not sufficiently complex and the network with IS hidden nodes is
After data pre-processing, comprehensive statistical analysis of the data were perfonned. The obtained results from statistical analysis were used together
180
---Controls
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Show
Preferences
operational data, but neural networks are not capable of extrapolation. They will give unreliable results when presented with new data that are not represented by the training data. Hence, despite the good results shown above, the use of the quality prediction is used as a part of the closed-loop control system sets special requirements on the reliability. The system needed to be developed further to determine when the predictor starts to extrapolate.
Help
................... ---."..... -
:_::;_=:;:-....t-.,_
-----j _ __J _ _ C:::::::J .............. c:::::iJ _ _ C:::::::J .-. ....... c::::::J
:.--... ....J..!!!!
Automatic detection of the extrapolation is based on another type of network, the radial basis function network (RBFN) (Moody, 1989; Leonard, 1992), trained with the same data as the BPN networks used in actual quality prediction. Cases that require extrapolation are automatically detected to avoid inaccurate predictions.
.
LI ME MUO DAY SOLIDS CONTENT
..
"
71 " ' - - - - - - - - - '
11
.,
3. NNs IN QUALITY PREDICTION AND CONTROL
Figure 1. An example of data filtering, scaling and selecting an appropriate network architecture
3.1 Intelligent system with embedded neural networks r-------~·------·--- ---------- --
.
Overall structure of the control system. The neural network predictor is one part of the intelligent system, . which is connected to the process through the basic automation system and plant wide information system. The plant wide information system is used to collect process measurements from the basic automation system. This is then used to calculate the average values and to archive data into the real time database (RTDB). The intelligent system is connected to the RTDB, providing access to all data, both the current values and history values as well as write-access to the regulatory control loops in the basic automation system The overall structure of the implemented system is shown in the Figure 4.
NNs ARCHITECTURE SElECTION 0.750 0.650 FITTING ERROR 0.550 0.450 0.350
t....... i...J.i...J.i;ii.lr' 5 10 15 8 NUMBER OF HIDDEN NODES
Figure 2. Fitting errors with different architectures
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Figure 3. Fitting results with 8 hidden nodes Figure 4. Overall structure of the intelligent system
overly complex for the problem at hand. Finally, the best architecture with one hidden layer and 8 hidden nodes was trained using all the available data. A comparison of the laboratory analysis and the neural network outputs is shown in Figure 3.
The Main functions of the intelligent system. intelligent control system performs high-level control functions to stabilize and optimize the 1ciln operation. The application includes the following main functions as shown also in Figure 5: (Jarvensivu and Ruunsunen, 1998)
Detection of the extrapolation. Neural networks have the ability to learn and adapt behavior of past
181
• •
• • •
basic engineering calculations, neural network models based feedforward control of the kiln rotation, draft fan speed and fuel flow rate, neural network based calculation of the target value for the temperature and excess oxygen, neural network based prediction of the residual CaC03 content, high level feedback corrections based on the fuzzy logic principles and the natural language rules
FAN.RPM. NNO + se + QC NNO-IC_ _ 'aI,,*,,).
se • 1(0, . CD" • HET). ~ ,.".,
oc
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KILN PRODUCTION RATE, t/h
Figure 6. Principle of the feedforward control Table 3. Examples of the rules used to determined feedback corrections Rules for hi h level corrections if CET high and HET low and 02 low then decrease FAN s
Draft fan Speed
Figure 5. Main functions of the intelligent system
Kiln Rotation
Feedforward control. Feedforward control of the kiln rotation, draft fan speed, and fuel flow rate are based on the neural network models which were developed using the same pre-processed data as the quality prediction. The models are used for changing the set point of the regulatory control loops during the production rate changes. Furthermore, the neural network models are used to determine bounds for the acceptable set point at the specific production rate. The principle of the feed forward control is show in Figure 6.
Fuel flow rate
•
if Kll.N load high then increase Kll.N
•
if MKT (high+increas.) and HET (high+increas.) then decrease FUEL flow rapidly if MKT (ok+increas.) and HET (Iow+increas.) then wait
•
CET = cold end temperature. MKT = middle kiln Temperature. HET = hot end temperature
High level adjustments. High level adjustments are functions designed to maximize the production capacity and energy efficiency of the kiln, and to optullize product quality and environmental performance.
Target value calculation and feedback corrections. Neural network models are also used to calculate target values for the kiln temperature profile, and the excess oxygen content of the flue gas. The input variables used for the models are delayed production rate and heavy fuel oil flow.
The throughput maximization module determines the kiln production rate. The main function of the module is to determine the maximum production rate the kiln can sustain under current conditions. The maximum sustainable production rate is determined based on the prediction of the kiln load. The kiln load is predicted with neural networks by using the draft in the feed end of the kiln and the power required for rotating the kiln. In addition, the module determines the required kiln production rate based on the current value and trend of the lime mud storage level.
Feedback corrections are based on the calculated target values, and the current process measurement and the change over the specified time period. The fuzzy logic principles and/or natural language type of rules determines required corrections. Figtrre 6 shows also how the feedback corrections are combined together with the feed forward part of the system. Some examples of the rules determining the required feedback correction are shown in Table 3.
The minimization of the energy consumption and environmental protection are taken into account in the same module. The module adjusts the target value for excess oxygen content and wash water of the lime mud filters based on the total reduced sulfur (TRS) emission level.
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The target value for excess oxygen is decreased with small steps to achieve improved energy efficiency (i.e. less cold air needed to be warmed up) if TRS emission level is low. In contrast, the target value for excess oxygen is increased to avoid environmental impact if the TRS emission level starts to increase. Similarly, wash water to the lime mud filters is reduced to achieve low moisture content of the lime mud fed into the kiln, if TRS emission is low. The amount of the wash water is increased to achieve better washing, if TRS emission level starts to increase.
The upper chart shows all data collected during the first month in operation. The lower focuses on the highlighted portion of the data. As the top portion shows, extrapolation was detected six times during the examined period. Overall, the developed neural network model is predicting the product quality fairl y well. RESIDUAL CAC03 PREDICTlON 7.5 e.5
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The optnruzation module of the product quality (residual Cac~ content of the reburned lime), schematically represented in Figure 7, minimizes variations by maintaining the middle kiln and burning end temperature at the desired target values .
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The product quality is maintained at the optimum level between 2 and 3 % by making adjustments for the middle kiln and hot end temperature targets. In addition to the laboratory analysis presented neural network prediction of the residual Cac~ content is used when the required adjustments are determined.
Figure 9 shows that under manual control the mean value of the excess oxygen was about 4 % whereas automatic control averaged around 2.5% excess oxygen. Reduction of the excess oxygen makes the kiln more energy efficient and decreases draft in the kiln. This consequentially reduces dust circulation in
3.2 Results The presented neural network predictor has been in use at the Pietarsaari pulp mill in Finland since March 1998 as part of the intelligent real-time control system, which has been in operation since April 1998. The neural network prediction (BPN) and laboratory analysis of the residual Cac~ content of the reburned lime are shown in Figure 8. Maximum hidden node activation of the RBFN is also shown in the same Figure. Maximum hidden node activation indicates the confidence of the prediction and can therefore be used for extrapolation detection.
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5. ACKNOWLEDGEMENTS This work was supported by UPM-Kymmene and the Academy of Finland. The authors wish to thank the staff of the Pietarsaari pulp mill for their valuable support and assistance during the field survey, as well as in the development and implementation stage of the intelligent control system
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Anderson 1. (1994). Getting the most from advanced process control, Chemical Engineering, March 1994, p. 78-89. Boullard, L. (1992). Application of Artificial Intelligence in Process Control, p. 455. Pergamon Press. Brambilla, A (1996). Estimate product quality with ANNs, Hydrocarbon Processing, September, 1996, p.61-66. Deshpande, P. (1996). Improve control with software monitoring technologies, Hydrocarbon Processing, September 1996, p. 81-88. Friedmann, P. (1995). Economics of control improvement, Instrument Society ofAmerica, p. 162. Hunt, K. (1992) Neural networks for control systems, Automatica, 28:6, p. 1083-1112. Jiirvensivu M. (1998a). Evaluation of various alternatives to reduce TRS emission at the lime kiln, TAPPI International Chemical recovery Conference, Tampa, FL, USA Jiirvensivu M. (1998b). Empirical lime kiln process modeling with neural networks, EUROSIM' 98 Congress, April 14-15, 1998, Espoo, Finland. Jiirvensivu M . and P. Ruusunen (1998) Intelligent lime-kiln control system, GUS'98 (Gensym Users Society Meeting), 13-15 May 1998, Newport, RI, USA Leonard, 1. (1992) Using radial basis function to approximate a function and its error bounds, IEEE Trans. Neural Network, 3:4, p. 624-627. Moody, 1. (1989). Fast learning in networks of locally-tuned processing units, Neural Computing, 1989:1, p.281-294. Pradeep, B. (1997) Predict difficult-ta-measure properties with neural analyzer, Control Engineering, July 1997, p. 55-56. Ramasamy, S. (1995). Consider neural networks for process identification, Hydrocarbon processing, June 1995, p.59-62. Sjoberg, J. (1995) Nonlinear black-box modeling in system identification, Automatica, 31:12, p. 16911724. Song J. (1993). Neural model predictive control for nonlinear chemical processes, Journal of Chemical Engineering of Japan, 268 :4, p . 347-354. Willis, M. (1992). Artificial neural networks in process estimation and control, Automatica, 28:6, p. 11811187.
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Figure 10. Variation of specific energy consumption in automatic (upper) and in manual (lower) control Also one-hour average values of the specific energy consumption and the actual and target value of the hot end temperature as a function of time. the feed-end of the kiln, which in turn increase the attainable maximum production capacity. Figure 9 presents also one-hour average values of the excess oxygen content and draft fan speed as a function of the time. The excess oxygen was maintained above the critical environmental level of 1.5%. As shown in Figure 10, manual control maintains the specific energy consumption between 4 .6 and 7.0 GJ/tcao. On automatic control it was 5.4 and 6.8 GJ/tcaO' The variation of the specific energy consumption when the intelligent system was in use was reduced over 30% compared to the manual mode. Reduction of the variation makes the kiln process more stabile and it will also significantly reduce lime quality variation. 4. CONCLUSIONS This paper has illustrated the ability of neural network technologies to model a multivariate nonlinear process. The neural network model predicted the quality sufficiently accurately for control. Furthermore the paper presents developed intelligent real-time control system with embedded neural networks, which has been successfully implemented at a Finnish pulp mill. Experience has shown that the experimental knowledge of the process behavior together with proper software tools can be utilized to improve process efficiency and produce considerable benefits both the economical and ecological respect.
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