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A knowledge-based system for the wastewater treatment plant
29
A Knowledge-Based System for the Wastewater Treatment Plant Kazuo MAEDA Chief Engineer, Mitsubishi Electric Corporation, 1-1-2 Wadasaki-cho, Hyogo-ku, Kobe, Hyogo, 652 Japan
This paper discusses a knowledge based system for the wastewater treatment plant. Our method gains from qualitative and experienced judgement of the operator in supervisory work such as set point scheduling, plant diagnosis and maintenance. First, we explain an architecture which consists of two parts such as an adaptive production system and a multimodal user interface e.g. video graphics, voice announcement, touch panel and mouse. Secondly we demonstrate the performance of our system. Finally we conclude that the proposed system would be a flexible and practical decision support system.
1. Introduction
This paper discusses a knowledge based decision support system for the wastewater treatment plant. Recently numerical control strategies such as dissolved oxygen (DO) control, mixed liquor suspended solid (MLSS) control and total sludge (TS) control, have been applied to real plant [1]. Our process, unlike chemical and pharmaceutical plants, has not good instrumentation and its behavior is not understood well. Thus supervisory control problems such as scheduling of set values, plant diagnosis and maintenance are solved by the experienced human operator. Our method tries to encode the so-called heuristics of the experienced operator for supervisory work. First, we explain the framework of our system that is implemented by a knowledge based system paradigm [4] and a human interface system paradigrn [5]. Secondly, we demonstrate a typical ex-
North-Holland Future GenerationComputerSystems5 (1989) 29-32
ample of operational guidance for the wastewater treatment plant. Finally, we discuss the performance, acceptability and limitations of our system with an overview of the future possibilities.
2. Architecture of the Knowledge-Based System for the Wastewater Treatment Plant We give the structure of the proposed system in its simplest form as shown in Fig. 1 [2,3]. The knowledge inference system consists of: 1. the numerical data base of on-line process, 2. the numerical algorithm base of process control, 3. the knowledge base of operational procedures, 4. the inference mechanism. Here a set of production rules represent all of the knowledge for solving the plant operation problems. The numerical simulator is also able to be utilized for determining the alternative controls on the algorithm base. The multimodal user interface accepts assertions and queries from the user by utilizing video
KNOWLEDGE BASED INFERENCE sY~r~4 .... 7 .......................................
t he I
ntnaerical d aDATABASE tabases for the prooess performance measure
ALGOR~ BASE 'process he n~nerical algorithm base for control & management a
~' ,,
BASE ' ' ' : set of production rules' for plant operation i KNONLt~DGE
forward s e a r c h for production rules I adaptive production, syste~ ..... ] L .................. #,.., .... .÷..................... .a MULTIMOOAL USER INTERFACE [ I. the status of input, output and control 12. the operational guides .I
THE WASTEHATERTREAT~ PLANT
Fig. 1. The architecture of the proposed knowledge based
0376-5075/89/$3.50 © 1989, ElsevierSciencePublishers B.V. (North-Holland)
system.
30
K. Maeda / Knowledge-Based System for Wastewater Treatment Plant
graphics, voice announcement, touch panel and mouse.
modified. The weights w O" (i = 1, n) change over time as follows: W i j ( t + 1) = W i j ( t ) + a R i R j - b W i j ( t )
3. Adaptive Inference Mechanism and Friendly Multimodal User Interface Operational procedures are usually represented by a hierarchical decision tree of questions and answers as shown in Fig. 2. But the operator is often too impatient to follow the complex decision tree and makes a mistake when the exact judgement is required urgently. In our proposed system, the above mentioned tree is represented by the production rules. If a node is the present question, then it has other nodes hanging from it - these are the next questions or final conclusion. We arrange the relation of the present question and next questions or conclusion in simple production rule. Here a production rule for the decision tree is represented as follows: rule(N, Quest, Ans, Next-quest, Pre-N, Type), where N is a rule number, Quest is the present question, Ans is the answer of the operator, Nextquest is the next question or a conclusion, Pre-N is previous triggered rule number and Type is the rule type such as a conclusion or a continued question. Thus rule N can be read as: For all x, if x is a Quest, previous triggered rule is Pre-N, rule type is Type, and all answer is Ans which holds for x, then new rule N is triggered and a Nextquest is asked. Thus a series of complex procedures is divided into many simple production modules. Next, we describe the basic idea of the adaptive production system. The pathway from rule i to rule j becomes eligible to have its weight
A n ~ est Arts1 /" ~'~.Questl Ans2/\ Ans3 ~" ~Quest2 ... ... °..
\
t co.cL s 0, l F i g 2 The decision tree of operational procedures for the wastewater treatment p l a n t
(1)
where w o ( t ) : the rate of connection between Rule i and Rule j at time t, a : positive constant reinforcing the rate of
W@, b
: positive constant diminishing the rate of
W/j, Ri : state variable of Rule i (1 or 0). Thus we can change the rank of rule firing and integrate some relational rules into one rule by utilizing the modified connection rate WO. In our work, we are attempting to develop user friendly multimodal communication. Our system can give the user proper commands immediately, deduced from production rules. User can ask further question for What (the meaning of word), Where (place of equipment), How (a method of action). The answer to question is given by voice announcement and video graphics.
4. A Computer Assisting Diagnosis and Restoration for the Wastewater Treatment Plant In the operation of the activated sludge process, there are many problems such as poor measuring devices or the varying sludge characteristics. Therefore the operation of the process diagnosis and restoration has been partly exercised manually by the skilled personnel. We demonstrate an example of application of our proposed system. Here is a short scenario: @What kind of the operational guidance do you request? ( D I A G N O S I S / R E S T O R I N G PROCEDURES: ) DIAGNOSIS @The numerical process data obtained from online sensor are as follows: COD 9.0 mg/1, DO 0.5 mg/1, MLSS 1200.0 mg/1, pH 6.8 @What about the oxygen utilization rate? (high/normal/low:) Why? @Rule102, Rule034 and Rule038 gave suggestive question. How?
K. Maeda / Knowledge-Based System for Wastewater Treatment Plant
Thus a consultation proceeds with our proposed system asking the user questions about the various observable attributes of the water and sludge quality including manual laboratory measurement. The required procedures are given plainly by the simple question and answering system. Similar typical display of the operational guides is shown in Fig. 3. Altogether about 100 production rules have been collected for this purpose.
@Effluent COD is normal from on-line information and so Rule102 fires. @DO(Dissolved Oxygen) is low from on-line information and so Rule034 fires. @MLSS is normal from on-line information and so Rule038 fires. @Are you satisfied with the above explanation? (yes/no:) yes @What about the oxygen utilization rate? (high/normal/low:) high @Do you try the adaptive mechanism? (yes/no:) yes @Adaptive rule deduces the following. @Instantaneous oxygen consumption rate? (high/normal:) high @Iodine consumption rate? (high/normal:) high @Dissolved ferrous ion? (high/normal:) high DIAGNOSIS / the sudden inflow of hazardous industrial wastes TREATMENT / check the influent industrial water @Will you continue? (yes/no:) no
5. Conclusion We have proposed a knowledge based decision support system for the wastewater treatment plant and demonstrated typical examples of operational guidance. Then we conclude the proposed system would be flexible and practical. The human operators will keep playing a central role in maintaining the high reliability of future biological systems. On the other hand, the importance of our system will increase the operator's cognitive capability. The proposed system will be used widely in wastewater treatment management, such as planning, control and manual operation guidance. For
I-
/ high / high -- Ro.lod? -norRal-t high -- Ro_ins? -noPeal \__low RP? -noreal \lou
31
|
1. on-line check
->OK!
<< Decayed Sludge >> 2. generation 0£ hgpotheses <>
->OK!
~HAT
3. selection o¢ hgpotheses bg orF-l|ne check ->OK!
\lov
On-line check
III
MHER[ HOM MHY ========
normal
Orr-i|ne c~eck
lo. I
nor*el
I
' )007
CP?
Tr?
TeNp?
~
4. diagnosis
->OK! )RCEMARO
5. selection 0£ operation
->OK!
S. execution 0£ operation
->OK!
FORMARO
Diagnosis I operation hlgh
high nornal
~
!
cause or Fett~'e :
Dacaged sludge Operation :
fe_lon?
increase Q, a Q~ i
Xill you continue? (yes/no):
Fig. 3. Typical display of operational g u i d a n c e .
32
K. Maeda / Know~edge-BasedSystem for Wastewater Treatment Plant
further research, we n e e d to i m p r o v e the k n o w l edge acquisition m e c h a n i s m such as a d a p t a t i o n a n d self-learning. T h e k n o w l e d g e b a s e d system will integrate the n u m e r i c a l controller. A deep m o d e l of the process a n d the i n s t r u m e n t a t i o n & c o n t r o l p l a n t has to b e d e v e l o p e d to deal with unforeseen events.
[2] [3]
[4] [5]
References [1] K. Maeda, M. Maeda, S. Osada, T. Kuwata and I. Nakahori. Feasibility of a new biomass control for full-scale waste-
water treatment plant, Proc. of 8th IFAC. XXII (1981) 134-139. K. Maeda, A knowledge based system for wastewater treatment process, Proc. ofgth IFAC, Vol. 4 (1984) 3251-3256. K. Maeda, An intelligent decision support system for wastewater treatment plant, Proc, of 26th SICE, Vol. 1 (1987) 7-8. P.H. Winston, Artificial Intelligence (Addison-Wesley, 1977). T. Ueda and T. Sakaguchi, Electronic manual for maintenance operator, Proc. of 1st SICE Knowledge Engineering Workshop (1983) 41-46.
A Knowledge-Based System for the Wastewater Treatment Plant Kazuo MAEDA Chief Engineer, Mitsubishi Electric Corporation, 1-1-2 Wadasaki-cho, Hyogo-ku, Kobe, Hyogo, 652 Japan
This paper discusses a knowledge based system for the wastewater treatment plant. Our method gains from qualitative and experienced judgement of the operator in supervisory work such as set point scheduling, plant diagnosis and maintenance. First, we explain an architecture which consists of two parts such as an adaptive production system and a multimodal user interface e.g. video graphics, voice announcement, touch panel and mouse. Secondly we demonstrate the performance of our system. Finally we conclude that the proposed system would be a flexible and practical decision support system.
1. Introduction
This paper discusses a knowledge based decision support system for the wastewater treatment plant. Recently numerical control strategies such as dissolved oxygen (DO) control, mixed liquor suspended solid (MLSS) control and total sludge (TS) control, have been applied to real plant [1]. Our process, unlike chemical and pharmaceutical plants, has not good instrumentation and its behavior is not understood well. Thus supervisory control problems such as scheduling of set values, plant diagnosis and maintenance are solved by the experienced human operator. Our method tries to encode the so-called heuristics of the experienced operator for supervisory work. First, we explain the framework of our system that is implemented by a knowledge based system paradigm [4] and a human interface system paradigrn [5]. Secondly, we demonstrate a typical ex-
North-Holland Future GenerationComputerSystems5 (1989) 29-32
ample of operational guidance for the wastewater treatment plant. Finally, we discuss the performance, acceptability and limitations of our system with an overview of the future possibilities.
2. Architecture of the Knowledge-Based System for the Wastewater Treatment Plant We give the structure of the proposed system in its simplest form as shown in Fig. 1 [2,3]. The knowledge inference system consists of: 1. the numerical data base of on-line process, 2. the numerical algorithm base of process control, 3. the knowledge base of operational procedures, 4. the inference mechanism. Here a set of production rules represent all of the knowledge for solving the plant operation problems. The numerical simulator is also able to be utilized for determining the alternative controls on the algorithm base. The multimodal user interface accepts assertions and queries from the user by utilizing video
KNOWLEDGE BASED INFERENCE sY~r~4 .... 7 .......................................
t he I
ntnaerical d aDATABASE tabases for the prooess performance measure
ALGOR~ BASE 'process he n~nerical algorithm base for control & management a
~' ,,
BASE ' ' ' : set of production rules' for plant operation i KNONLt~DGE
forward s e a r c h for production rules I adaptive production, syste~ ..... ] L .................. #,.., .... .÷..................... .a MULTIMOOAL USER INTERFACE [ I. the status of input, output and control 12. the operational guides .I
THE WASTEHATERTREAT~ PLANT
Fig. 1. The architecture of the proposed knowledge based
0376-5075/89/$3.50 © 1989, ElsevierSciencePublishers B.V. (North-Holland)
system.
30
K. Maeda / Knowledge-Based System for Wastewater Treatment Plant
graphics, voice announcement, touch panel and mouse.
modified. The weights w O" (i = 1, n) change over time as follows: W i j ( t + 1) = W i j ( t ) + a R i R j - b W i j ( t )
3. Adaptive Inference Mechanism and Friendly Multimodal User Interface Operational procedures are usually represented by a hierarchical decision tree of questions and answers as shown in Fig. 2. But the operator is often too impatient to follow the complex decision tree and makes a mistake when the exact judgement is required urgently. In our proposed system, the above mentioned tree is represented by the production rules. If a node is the present question, then it has other nodes hanging from it - these are the next questions or final conclusion. We arrange the relation of the present question and next questions or conclusion in simple production rule. Here a production rule for the decision tree is represented as follows: rule(N, Quest, Ans, Next-quest, Pre-N, Type), where N is a rule number, Quest is the present question, Ans is the answer of the operator, Nextquest is the next question or a conclusion, Pre-N is previous triggered rule number and Type is the rule type such as a conclusion or a continued question. Thus rule N can be read as: For all x, if x is a Quest, previous triggered rule is Pre-N, rule type is Type, and all answer is Ans which holds for x, then new rule N is triggered and a Nextquest is asked. Thus a series of complex procedures is divided into many simple production modules. Next, we describe the basic idea of the adaptive production system. The pathway from rule i to rule j becomes eligible to have its weight
A n ~ est Arts1 /" ~'~.Questl Ans2/\ Ans3 ~" ~Quest2 ... ... °..
\
t co.cL s 0, l F i g 2 The decision tree of operational procedures for the wastewater treatment p l a n t
(1)
where w o ( t ) : the rate of connection between Rule i and Rule j at time t, a : positive constant reinforcing the rate of
W@, b
: positive constant diminishing the rate of
W/j, Ri : state variable of Rule i (1 or 0). Thus we can change the rank of rule firing and integrate some relational rules into one rule by utilizing the modified connection rate WO. In our work, we are attempting to develop user friendly multimodal communication. Our system can give the user proper commands immediately, deduced from production rules. User can ask further question for What (the meaning of word), Where (place of equipment), How (a method of action). The answer to question is given by voice announcement and video graphics.
4. A Computer Assisting Diagnosis and Restoration for the Wastewater Treatment Plant In the operation of the activated sludge process, there are many problems such as poor measuring devices or the varying sludge characteristics. Therefore the operation of the process diagnosis and restoration has been partly exercised manually by the skilled personnel. We demonstrate an example of application of our proposed system. Here is a short scenario: @What kind of the operational guidance do you request? ( D I A G N O S I S / R E S T O R I N G PROCEDURES: ) DIAGNOSIS @The numerical process data obtained from online sensor are as follows: COD 9.0 mg/1, DO 0.5 mg/1, MLSS 1200.0 mg/1, pH 6.8 @What about the oxygen utilization rate? (high/normal/low:) Why? @Rule102, Rule034 and Rule038 gave suggestive question. How?
K. Maeda / Knowledge-Based System for Wastewater Treatment Plant
Thus a consultation proceeds with our proposed system asking the user questions about the various observable attributes of the water and sludge quality including manual laboratory measurement. The required procedures are given plainly by the simple question and answering system. Similar typical display of the operational guides is shown in Fig. 3. Altogether about 100 production rules have been collected for this purpose.
@Effluent COD is normal from on-line information and so Rule102 fires. @DO(Dissolved Oxygen) is low from on-line information and so Rule034 fires. @MLSS is normal from on-line information and so Rule038 fires. @Are you satisfied with the above explanation? (yes/no:) yes @What about the oxygen utilization rate? (high/normal/low:) high @Do you try the adaptive mechanism? (yes/no:) yes @Adaptive rule deduces the following. @Instantaneous oxygen consumption rate? (high/normal:) high @Iodine consumption rate? (high/normal:) high @Dissolved ferrous ion? (high/normal:) high DIAGNOSIS / the sudden inflow of hazardous industrial wastes TREATMENT / check the influent industrial water @Will you continue? (yes/no:) no
5. Conclusion We have proposed a knowledge based decision support system for the wastewater treatment plant and demonstrated typical examples of operational guidance. Then we conclude the proposed system would be flexible and practical. The human operators will keep playing a central role in maintaining the high reliability of future biological systems. On the other hand, the importance of our system will increase the operator's cognitive capability. The proposed system will be used widely in wastewater treatment management, such as planning, control and manual operation guidance. For
I-
/ high / high -- Ro.lod? -norRal-t high -- Ro_ins? -noPeal \__low RP? -noreal \lou
31
|
1. on-line check
->OK!
<< Decayed Sludge >> 2. generation 0£ hgpotheses <
->OK!
~HAT
3. selection o¢ hgpotheses bg orF-l|ne check ->OK!
\lov
On-line check
III
MHER[ HOM MHY ========
normal
Orr-i|ne c~eck
lo. I
nor*el
I
' )007
CP?
Tr?
TeNp?
~
4. diagnosis
->OK! )RCEMARO
5. selection 0£ operation
->OK!
S. execution 0£ operation
->OK!
FORMARO
Diagnosis I operation hlgh
high nornal
~
!
cause or Fett~'e :
Dacaged sludge Operation :
fe_lon?
increase Q, a Q~ i
Xill you continue? (yes/no):
Fig. 3. Typical display of operational g u i d a n c e .
32
K. Maeda / Know~edge-BasedSystem for Wastewater Treatment Plant
further research, we n e e d to i m p r o v e the k n o w l edge acquisition m e c h a n i s m such as a d a p t a t i o n a n d self-learning. T h e k n o w l e d g e b a s e d system will integrate the n u m e r i c a l controller. A deep m o d e l of the process a n d the i n s t r u m e n t a t i o n & c o n t r o l p l a n t has to b e d e v e l o p e d to deal with unforeseen events.
[2] [3]
[4] [5]
References [1] K. Maeda, M. Maeda, S. Osada, T. Kuwata and I. Nakahori. Feasibility of a new biomass control for full-scale waste-
water treatment plant, Proc. of 8th IFAC. XXII (1981) 134-139. K. Maeda, A knowledge based system for wastewater treatment process, Proc. ofgth IFAC, Vol. 4 (1984) 3251-3256. K. Maeda, An intelligent decision support system for wastewater treatment plant, Proc, of 26th SICE, Vol. 1 (1987) 7-8. P.H. Winston, Artificial Intelligence (Addison-Wesley, 1977). T. Ueda and T. Sakaguchi, Electronic manual for maintenance operator, Proc. of 1st SICE Knowledge Engineering Workshop (1983) 41-46.