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Mathematical modelling and expert system integration for optimum control strategy of MSF desalination plants
Desalination, 97 (1994) 547-554 Elsevier Science B.V., Amsterdam -
547 Printed in The Netherlands
Mathematical modelling and expert system integration for optimum control strategy of MSF desalination plants B. Fumagalli and E. Ghiazza IRITECNA, Via di Francis 1, 16186 Genova (Italy)
SUMMARY
On the basis of the experience acquired with the design and operation of the process control system of Umm Al Nar East desalination plants (in operation since May 1988), a further development of this kind of control system is presented. The possible improvements derive from a suitable subdivision of tasks between a traditional algorithmic system and an expert system based on artificial intelligence techniques. In the first one, calculations are performed by means of mathematical models to evaluate the main process parameters, while the actuation of the calculated set points and the management of the corresponding plant transient conditions are carried out by the expert system using the rules in its knowledge base.
INTRODUCTION
One of the main problems in the automatic control of a desalination plant is the availability of control algorithms able to manage all the situations taking place during the load transients. Due to the close connection between the physical phenomena governing the flashing of brine, the condensation of steam outside the tube bundles, the heating of brine in the tubes and the brine hydrodynamic behavior in the stages, a sequence of checks on various OOll-9X4/94/$07.00 0 1994 Elsevier Science B.V. All rights reserved. SSDIOOll-9164(94)00113-3
548
process variables is required in order to avoid effects such as the brine blow-through or pile-up due to low/high levels in the stages, undesired fluctuations of seawater temperature at the outlet of the reject section, and of the TBT. The results of these checks bring the system to adjust the sequence and the duration of the set-points variation steps until the final distillate production flow rate is reached. IRITECNA developed a control system based on this philosophy in operation since 1988 at the Umm Al Nar East desalination plant in Abu Dhabi. The complete description of the control system is reported in [ 1,2] while the results of one year of operation for the plant are reported in [3]. In order to obtain an efficient and reliable operation of the control system, a continuous tuning of its performances was carried out for a period of about 3 months, allowing the process engineers to calibrate the main parameters and to adapt the control logic to the desalination plant characteristics. In recent years the expert systems diffusion in the industrial field grew up due to their special features. Unlike traditional procedural systems, the rules-based systems have the following advantages: quite general operating rules can be defined in the system l l operating rules can be activated only if necessary by the control algorithm (inference engine) l qualitative evaluation of events, that is, familiar to the operator, can be used l new rules can be easily inserted into the system without changing the existing architecture A new control philosophy is presented in the following sections, involving an expert system for the management of the transient operations while the data treatment, monitoring functions, and set-points calculation are performed by a traditional algorithmic system.
OPTIMIZATION
SYSTEM
It calculates the set points of the main variables for the control of the production on the basis of the different operating conditions of the plant. Within the optimization system software architecture, the following main functions can be identified:
549
/ I I
-1
Fig. 1. Data acquisition, data treatment and reconciliation, set points calculation.
process parameters updating, and
Fig. 1 shows the relationships and data exchange among the main functional blocks of the system in one of the possible solutions for the software configuration. The functions of each block are explained in detail below. Data acquisition and treatment
The measured values of the main variables are taken from the plant instrumentation with a predetermined frequency. Usually these values are already filtered since normal data treatment procedures such as a check of the limit values, integration, conversion to engineering units, and linearization of characteristics are already present in the instrumentation and basic automation system. The treated values, however, are not available for the process calculations since they do not fulfill the system constraints (e.g., the heat and mass balances and heat exchange equations of the plant subsystems). The information obtained is not completely reliable due to the stochastic errors and to the biases usually present in the measurements. Moreover, not all the process variables can be measured. In order to have a set of process variables, fulfilling the system constraints, and an estimation of unmeasurable variables, data reconciliation
550
methods are available. The aim of data reconciliation is to: (1) improve the reliability of measurements, (2) to estimate unmeasurable variables, and (3) to estimate instrumentation biases. Updating tifprocess parameters
The performance of the desalination plant is affected by a number of external parameters, necessitating the modification of the set points of the plant during its operating life in order to keep the same production rate. These parameters can be divided as follows: l variations in the seawater temperature that are a seasonal perturbation of the operating conditions and can be detected by the temperature measurement; l increasing the fouling in the evaporator and brine heater tubes for the same production rate due to the presence of dissolved solids in the seawater. Since the mathematical model of the desalination unit is based on the stages heat exchange equations, it is necessary to know the fouling degree in each stage. For this purpose a periodic estimation can be carried out based on the measured values of the stage temperatures and the ones calculated by the model. The updated values can be used within the expert system to make decisions for plant operation management. Mathematical model
The set points values to use in order to keep the desired distillate production are calculated using a steady-state mathematical model. The basic equations describing the behavior of the desalination unit are the heat and mass balance equations and the heat exchange equation of each stage. The variation of the fouling degree in each stage affecting the value of the set points is considered in the calculation. The following variables mainly affect the distillate product flow rate: brine top temperature T_ and brine recirculation flow rate IV,. The same value of distillate flow rate can be achieved with different combinations of T,, and W, for a given plant condition. The choice of the values to be set affects the behavior of the desalination plant, making the need to define a rule for this choice. The highest value of the performance ratio of the plant
551
is achieved with the maximum value of T_ and the minimum value of IV,. The necessity to avoid a blow-through between contiguous stages requires defining some constraints for this choice. Other constraints are the maximum operating temperature of the anti-scale in use in the plant and the operating limits of the mechanical equipment such as the pumps. A solution of the problem is to insert in the model the reference operating curves of the plant that can be modified by the model itself to account for the modifications of external parameters. This solution is easy to be implemented, but it takes time for the tuning of the best values to be inserted in the operating curves. Moreover, no optimization of plant operations is achieved with this method. Another solution is to build up a cost function for the system and to choose the set points of T,, and W, which minimize the function. This approach to the problem has the advantage of being more flexible because several variables can be considered in the cost function. On the other hand, the mathematical approach is more complex, the calculations are timeconsuming, and a powerful machine has to be provided as hardware support. This problem becomes more and more critical if variables other than T_ and IV, are involved in the calculation.
EXPERT SYSTEM
The aim of an expert system is to supervise the operation and the on-line control of a plant, supplying the degree of knowledge of a plant engineer or of a skilled operator. In such a way elements of knowledge derived from experience that can hardly be handled using traditional logic sequences and algorithms become part of the control strategy. An important element of the rules of the knowledge base is the possibility of including qualitative evaluation of the phenomena involved in plant behavior. The main functions of an expert system within the control system a desalination plant can be:
l
plant operations management transients management trouble-shooting and diagnostics
l
The plant operations management gets information about events such as: seawater temperature fluctuations
l l
l l l l
low 1.~. steam amount to the desalination unit high distillate conductivity pump trouble arising abnormal brine or distillate levels
As these events may produce a change in the plant operating environment, the system takes the proper actions (variation of the operating targets and of the boundary conditions) to keep the plant running in the best way. The targets of the control strategy are changed as a consequence of the checks carried out on plant operating conditions (e.g., in case of low 1.~. steam availability, the target production is set to the maximum reachable value instead of the required one; in case of high distillate conductivity during a load change, the brine recirculation is limited while the problem exists). During the plant transients, all the checks and operations to guarantee the necessary safety of equipment are executed by the system. The following typical problems can be handled in this case selecting the proper sequence and duration of steps for the change of the set points: for a load increase, an interaction exists between the variation of the brine top temperature set point and the brine recirculation set point. Due to the different responses of the system to the change of these set points, an increase of the brine recirculation may lead to a decrease of the temperature at the outlet of the brine heater, even in the presence of a TBT set point increase. Consequently, the heating of the brine is delayed, and extra steam to the brine heater is necessary. a fast increase of the seawater flow rate can lead to a decrease of the seawater temperature at the reject outlet and of the make-up temperature as a consequence. In order to avoid this problem, a suitable interaction between the seawater flow rate and temperature at the reject inlet is suggested. Examples of control rules as shown in Fig. 2. The trouble-shooting and diagnostic functions give the operator information about the abnormal behavior of the process variables involved in the automatic operation of the plant. The expert system analyzes the main process variables, getting information from their values, their trends and their relations, in order to recognize anomalous situations. The results of the analysis are also used to take the correct control actions at the plant.
- the level of first stage is normal - brine temp. is increasing - load increase is in starting phase
set the set point of brine recirculation to set point +0
- load increase is in progress - the level of first stage is decreasing - brine temperaturt is increasing
set the set point of brine recirculation to set point +delta
Fig. 2. Examples of control rules.
The expert system configuration can also include the automatic control of the overall plant, but this target requires the design of the instrumentation and control system in close connection with the expert system. The possibility of using results of the existent mathematical models, however, is a powerful tool giving the system the possibility of correlating the variables involved in the analysis.
CONCLUSIONS
An alternative way for the control of a desalination plant can be based on artificial intelligence techniques. An expert system provides the designer with tools to insert into the control system qualitative knowledge rules, usually followed by skilled operators in the operation of the plant. A particular advantage in the use of an expert system is the possibility of easily updating the knowledge base by adding new rule during the operation of the plant. The knowledge of the engineers plays an important role in the creation and updating of the data base. The presence in the control system of quantitative relations among the process variables is, however, useful for optimization purposes and for the comparison between the actual and desired values.
554
REFERENCES 1 2 3 4 5 6 7
S. Arazzini and D.M.K. Fareigh, Desalination, 55 (1985) 91. R. Cirelli, B. Fumagalli, E. Ghiazza and E. Longoni, Controllo di process0 di un impianto di dissalazione, in: Proc., 21st International Conference, BIAS, 1987. S. Rebagliati, E. Ghiazza and KS. Abueida, Desalination, 75 (1989) 149. McGhee, Grimble and Mowforth, Knowledge-based systems for industrial control, in: IEEE Control Engineering Series 44. R.S.H. Mah and A.C. Tamhane, AIChem J, 28 (1982) 828. S. Narasimhan and R.S.H. Mah, AIChem J, 33 (1987) 1514. A. Batistoni Ferrara, P. Fontana, E. Longoni, et al., An expert system for operator’s support in the control of a multistand pipe mill plant, in: Automazione e strumentazione, November, 1989.
547 Printed in The Netherlands
Mathematical modelling and expert system integration for optimum control strategy of MSF desalination plants B. Fumagalli and E. Ghiazza IRITECNA, Via di Francis 1, 16186 Genova (Italy)
SUMMARY
On the basis of the experience acquired with the design and operation of the process control system of Umm Al Nar East desalination plants (in operation since May 1988), a further development of this kind of control system is presented. The possible improvements derive from a suitable subdivision of tasks between a traditional algorithmic system and an expert system based on artificial intelligence techniques. In the first one, calculations are performed by means of mathematical models to evaluate the main process parameters, while the actuation of the calculated set points and the management of the corresponding plant transient conditions are carried out by the expert system using the rules in its knowledge base.
INTRODUCTION
One of the main problems in the automatic control of a desalination plant is the availability of control algorithms able to manage all the situations taking place during the load transients. Due to the close connection between the physical phenomena governing the flashing of brine, the condensation of steam outside the tube bundles, the heating of brine in the tubes and the brine hydrodynamic behavior in the stages, a sequence of checks on various OOll-9X4/94/$07.00 0 1994 Elsevier Science B.V. All rights reserved. SSDIOOll-9164(94)00113-3
548
process variables is required in order to avoid effects such as the brine blow-through or pile-up due to low/high levels in the stages, undesired fluctuations of seawater temperature at the outlet of the reject section, and of the TBT. The results of these checks bring the system to adjust the sequence and the duration of the set-points variation steps until the final distillate production flow rate is reached. IRITECNA developed a control system based on this philosophy in operation since 1988 at the Umm Al Nar East desalination plant in Abu Dhabi. The complete description of the control system is reported in [ 1,2] while the results of one year of operation for the plant are reported in [3]. In order to obtain an efficient and reliable operation of the control system, a continuous tuning of its performances was carried out for a period of about 3 months, allowing the process engineers to calibrate the main parameters and to adapt the control logic to the desalination plant characteristics. In recent years the expert systems diffusion in the industrial field grew up due to their special features. Unlike traditional procedural systems, the rules-based systems have the following advantages: quite general operating rules can be defined in the system l l operating rules can be activated only if necessary by the control algorithm (inference engine) l qualitative evaluation of events, that is, familiar to the operator, can be used l new rules can be easily inserted into the system without changing the existing architecture A new control philosophy is presented in the following sections, involving an expert system for the management of the transient operations while the data treatment, monitoring functions, and set-points calculation are performed by a traditional algorithmic system.
OPTIMIZATION
SYSTEM
It calculates the set points of the main variables for the control of the production on the basis of the different operating conditions of the plant. Within the optimization system software architecture, the following main functions can be identified:
549
/ I I
-1
Fig. 1. Data acquisition, data treatment and reconciliation, set points calculation.
process parameters updating, and
Fig. 1 shows the relationships and data exchange among the main functional blocks of the system in one of the possible solutions for the software configuration. The functions of each block are explained in detail below. Data acquisition and treatment
The measured values of the main variables are taken from the plant instrumentation with a predetermined frequency. Usually these values are already filtered since normal data treatment procedures such as a check of the limit values, integration, conversion to engineering units, and linearization of characteristics are already present in the instrumentation and basic automation system. The treated values, however, are not available for the process calculations since they do not fulfill the system constraints (e.g., the heat and mass balances and heat exchange equations of the plant subsystems). The information obtained is not completely reliable due to the stochastic errors and to the biases usually present in the measurements. Moreover, not all the process variables can be measured. In order to have a set of process variables, fulfilling the system constraints, and an estimation of unmeasurable variables, data reconciliation
550
methods are available. The aim of data reconciliation is to: (1) improve the reliability of measurements, (2) to estimate unmeasurable variables, and (3) to estimate instrumentation biases. Updating tifprocess parameters
The performance of the desalination plant is affected by a number of external parameters, necessitating the modification of the set points of the plant during its operating life in order to keep the same production rate. These parameters can be divided as follows: l variations in the seawater temperature that are a seasonal perturbation of the operating conditions and can be detected by the temperature measurement; l increasing the fouling in the evaporator and brine heater tubes for the same production rate due to the presence of dissolved solids in the seawater. Since the mathematical model of the desalination unit is based on the stages heat exchange equations, it is necessary to know the fouling degree in each stage. For this purpose a periodic estimation can be carried out based on the measured values of the stage temperatures and the ones calculated by the model. The updated values can be used within the expert system to make decisions for plant operation management. Mathematical model
The set points values to use in order to keep the desired distillate production are calculated using a steady-state mathematical model. The basic equations describing the behavior of the desalination unit are the heat and mass balance equations and the heat exchange equation of each stage. The variation of the fouling degree in each stage affecting the value of the set points is considered in the calculation. The following variables mainly affect the distillate product flow rate: brine top temperature T_ and brine recirculation flow rate IV,. The same value of distillate flow rate can be achieved with different combinations of T,, and W, for a given plant condition. The choice of the values to be set affects the behavior of the desalination plant, making the need to define a rule for this choice. The highest value of the performance ratio of the plant
551
is achieved with the maximum value of T_ and the minimum value of IV,. The necessity to avoid a blow-through between contiguous stages requires defining some constraints for this choice. Other constraints are the maximum operating temperature of the anti-scale in use in the plant and the operating limits of the mechanical equipment such as the pumps. A solution of the problem is to insert in the model the reference operating curves of the plant that can be modified by the model itself to account for the modifications of external parameters. This solution is easy to be implemented, but it takes time for the tuning of the best values to be inserted in the operating curves. Moreover, no optimization of plant operations is achieved with this method. Another solution is to build up a cost function for the system and to choose the set points of T,, and W, which minimize the function. This approach to the problem has the advantage of being more flexible because several variables can be considered in the cost function. On the other hand, the mathematical approach is more complex, the calculations are timeconsuming, and a powerful machine has to be provided as hardware support. This problem becomes more and more critical if variables other than T_ and IV, are involved in the calculation.
EXPERT SYSTEM
The aim of an expert system is to supervise the operation and the on-line control of a plant, supplying the degree of knowledge of a plant engineer or of a skilled operator. In such a way elements of knowledge derived from experience that can hardly be handled using traditional logic sequences and algorithms become part of the control strategy. An important element of the rules of the knowledge base is the possibility of including qualitative evaluation of the phenomena involved in plant behavior. The main functions of an expert system within the control system a desalination plant can be:
l
plant operations management transients management trouble-shooting and diagnostics
l
The plant operations management gets information about events such as: seawater temperature fluctuations
l l
l l l l
low 1.~. steam amount to the desalination unit high distillate conductivity pump trouble arising abnormal brine or distillate levels
As these events may produce a change in the plant operating environment, the system takes the proper actions (variation of the operating targets and of the boundary conditions) to keep the plant running in the best way. The targets of the control strategy are changed as a consequence of the checks carried out on plant operating conditions (e.g., in case of low 1.~. steam availability, the target production is set to the maximum reachable value instead of the required one; in case of high distillate conductivity during a load change, the brine recirculation is limited while the problem exists). During the plant transients, all the checks and operations to guarantee the necessary safety of equipment are executed by the system. The following typical problems can be handled in this case selecting the proper sequence and duration of steps for the change of the set points: for a load increase, an interaction exists between the variation of the brine top temperature set point and the brine recirculation set point. Due to the different responses of the system to the change of these set points, an increase of the brine recirculation may lead to a decrease of the temperature at the outlet of the brine heater, even in the presence of a TBT set point increase. Consequently, the heating of the brine is delayed, and extra steam to the brine heater is necessary. a fast increase of the seawater flow rate can lead to a decrease of the seawater temperature at the reject outlet and of the make-up temperature as a consequence. In order to avoid this problem, a suitable interaction between the seawater flow rate and temperature at the reject inlet is suggested. Examples of control rules as shown in Fig. 2. The trouble-shooting and diagnostic functions give the operator information about the abnormal behavior of the process variables involved in the automatic operation of the plant. The expert system analyzes the main process variables, getting information from their values, their trends and their relations, in order to recognize anomalous situations. The results of the analysis are also used to take the correct control actions at the plant.
- the level of first stage is normal - brine temp. is increasing - load increase is in starting phase
set the set point of brine recirculation to set point +0
- load increase is in progress - the level of first stage is decreasing - brine temperaturt is increasing
set the set point of brine recirculation to set point +delta
Fig. 2. Examples of control rules.
The expert system configuration can also include the automatic control of the overall plant, but this target requires the design of the instrumentation and control system in close connection with the expert system. The possibility of using results of the existent mathematical models, however, is a powerful tool giving the system the possibility of correlating the variables involved in the analysis.
CONCLUSIONS
An alternative way for the control of a desalination plant can be based on artificial intelligence techniques. An expert system provides the designer with tools to insert into the control system qualitative knowledge rules, usually followed by skilled operators in the operation of the plant. A particular advantage in the use of an expert system is the possibility of easily updating the knowledge base by adding new rule during the operation of the plant. The knowledge of the engineers plays an important role in the creation and updating of the data base. The presence in the control system of quantitative relations among the process variables is, however, useful for optimization purposes and for the comparison between the actual and desired values.
554
REFERENCES 1 2 3 4 5 6 7
S. Arazzini and D.M.K. Fareigh, Desalination, 55 (1985) 91. R. Cirelli, B. Fumagalli, E. Ghiazza and E. Longoni, Controllo di process0 di un impianto di dissalazione, in: Proc., 21st International Conference, BIAS, 1987. S. Rebagliati, E. Ghiazza and KS. Abueida, Desalination, 75 (1989) 149. McGhee, Grimble and Mowforth, Knowledge-based systems for industrial control, in: IEEE Control Engineering Series 44. R.S.H. Mah and A.C. Tamhane, AIChem J, 28 (1982) 828. S. Narasimhan and R.S.H. Mah, AIChem J, 33 (1987) 1514. A. Batistoni Ferrara, P. Fontana, E. Longoni, et al., An expert system for operator’s support in the control of a multistand pipe mill plant, in: Automazione e strumentazione, November, 1989.