Biotech: a real-time application of artificial intelligence for fermentation processes

CoatrotEng. Practice,Vol. 1, No. 2, pp. 315-321, 1993

0967-0661/93 $6.00 + 0.00 © 1993 Pm'gamonPrms Ltd

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BIOTECH: A REAL-TIME APPLICATION OF ARTIFICIAL INTELLIGENCE FOR FERMENTATION PROCESSES J.P. Steyer*,**, I. Queinnec*,** and D. Simoes** *Laboratoire d'Automatique et d'Analyse des Systdmes de CNRS, 7 Avenue du Colonel Roche, F-31077 Toulouse Cedex, France **Centre de Transfert en Biotechnologie et Microbiologie de 1"UPS-INSAT, Avenue de Rangueil, F.31077 Toulouse Cedex, France

~bstract. The puq~ose of this work was to exploit the main features and the advantages of qualitative physics. The influencesamong the process variables were modelled with the help of a causal graph, using qualitative operators. The reasoning about the standard state retraced the causal path explaining the observed behaviors accordingto the trends of the observable variables. Conflictsituationscould be solved using temporal notions. Those concepts were tailored to fermentationprocesses. An on-line Expert System (650 rules) has been built with the aim of derivingthe behaviorof the process. Kevwords. Expert Systems; qualitative modeling; qualitative reasoning; causal graphs; fermentation processes; real time computersystems. "reasoning" is utilized to give an explanation of the changes in variables. Its specific advantage is to control the process, even without accurate equations. This objective is continued using qualitative physics. A qualitative process modeling is performed which is exploited by the expert system rules. By comparing the actual changes and the a p r i o r i ones, the expert system gives a symbolic meaning to the signals and exploits this in order to decide on the best course of action.

1.INTRODUCTION In recent years, there has been strong interest in the application of modern control theories to fedbatch fermentation processes. Until the last decade, those studies essentially concerned the use of optimal control theories (Hong, 1986; Ohno and others, 1976). They were based on a complex mathematical modeling of the process, which is itself a field of interest for automaticians. Nowadays more and more studies develop linear and nonlinear adaptive schemes, which allow the modeling problem to be bypassed (Bastin and Dochain, 1985; Queinnec and others, 1991). However, the automatician has to cope with the system complexity, low reproduceability of experimentations and lack of avalaible sensors, to say nothing of the misunderstanding of some events.

In the first section, the qualitative physics tools developed are presented, and more particularly the causal graph associated with the process, the standard state reasoning and the reasoning using temporal aspects. After a brief description of the fedbatch fermentation process, the qualitative process modeling is performed, and qualitative operators are defined and described. In the last section Biotech (a real time expert system for fedbatch fermentation process monitoring and control) is presented, and simulation and experimental results are discussed.

An alternative approach for on-line monitoring and control of fedbatch fermentation processes is proposed, using qualitative physics and expert systems methodologies. Very few results have been presented using these theories as compared to optimal and adaptive control ones. IFCONS can, however, be held up as an example. It is an Intelligent Fermentation CONtrol System, which functions as a supervisor to a process controller (Asama and others, 1988). BIOACS (BIO Advanced Control System) is an automated system for fermentation process monitoring using expert system strategy and a data base (Cooney and others, 1988). Neither of these introduces qualitative physics theory to reach their objective.

2. QUALITATIVE PHYSICS APPROACH Classical mathematical modeling generally leads to an action model, which consists of a set of complex nonlinear differential equations. The model state variables, i.e. macroscopic process variables, are not always measurable (lack of available sensors, development costs). Our objective consists in using the on-line measured variables (essentially environment variables) to establish the system physiological state. With this end in view, it is a matter of modeling the influences between variables and the

The goal of this study is mainly to analyze and to explain the fermentation state by observing the measurements. Most of the expert system

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rules of the process behavior. Finally the best intervention strategy has to be determined in real time. An analysis of the macroscopic state is performed and the causes which may have induced this state are searched by propagation of the influences at a microscopic level. Causal modeling is then introduced, which allows one to establish the causal graph where influences between variables are modelled. d e f i n i t i o n : (Steyer, 1991) A model allowing the visualisation of the process variables causal influences with a control objective is defined by: a working criterion to describe the process qualitative state, an oriented graph whose nodes represent the pertinent variables of the modelled system, and whose edges represent the causal links between those variables, c> a vocabulary associated with the variables' semantics in order to describe their changes. There are five kinds of signals pointed out on this causal graph (physical values): Input signals, which correspond to process measurements. Observation variables (interpretation of the state and process dynamics) should be distinguished from action variables (actions previously made). z, Observations, representing qualitative information given by a human operator. They only augment observation variables in order to validate reasoning. c> Internal signals, which represent the microscopic state compared to the previous ones that proceed from macroscopic observations. They correspond to either process quantities or the results of process activity. The working criterion. Output signals which are actions to be taken. They are only instructions to the operator (to be distinguished from the action variables), and are not represented on the causal graph. These signals present considerable information. They represent a set of data such as trend and duration of a variation, prediction of the next evolution, cause of a variation, dissolute energy (Steyer, 1991)... A set of qualitative values is defined for each parameter, to represent its vocabulary. From the definition of the causal modeling a reasoning can be developed, whose goal deals with the analysis and the interpretation of the global physical system in a real-time evolving environment, by observing variations of the indirect variables (i.e. measurable effects of nonmeasurable causes). The observation variables (input signals) are the basis of this reasoning. The principle is the following: c> Translation of observation variables into qualitative values.

Recognition of the physical process situation by comparison between the observed state and the standard one (reasoning on the standard state). Z~ Interpretation of this state (cause of the state). z~ Eventually, definition of an admissible waiting time when several possible causes for the same event exist (reasoning using temporal aspects). If during this time the list of possible causes becomes a unique element, this will present no further problems. In the other case, the date and the nature of the problem are recorded. c> Propagation of the recognized cause in the causal graph to characterize the microscopic states. Proposals for operator actions, with respect to the control objective. 2.1. Reasoning, on the standard state A standard state can be associated with each signal. It can be the setpoint of a controlled variable, the best variation of a controlled variable, the expected evolution of a free variable... To any observed situation one can associate a list of possible causes. An interpretation procedure is then defined to find the cause of an observed state by comparison with the standard state. First concordance (co), resemblance (re) and opposition (op) relations are defined. The interpretation procedure can then be established, after comparison between the standard and observed states: z~ If there is concordance, the system state is due to the cause associated to the standard state. If there is resemblance, the system state cause is the logical negation of the cause associated to the standard state. z~ If there is opposition, the system state cause is the antagonistic cause of the standard state one. If several causes put a variable in a same state, this state is defined with an .unsolved interpretation. These causes can then be reduced by comparing possible causes of the state (filtering of inconsistent states). More information about the interpretation procedure of the standard state reasoning is given in (Steyer, 1991). The definition of the standard state can be a complex problem. The process situation has to be recognized and analyzed, and the standard state changed according to the system evolution. 2.2. Reasonin~ usin~ temtx)ral asoects Several aspects of temporal notions must be distinguished. This paper is concerned with a temporal reasoning which completes the standard state reasoning previously detailed. An appropriate modeling of precedence, recovery and simultaneous notions between facts, events and processes is necessary in any causality relation. We

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BIOTECH are concerned with the instant algebra which considers the instant as a temporal ‘unit (to be compared to interval algebra). The possible primary values between two instants are before, same or after. The reasoning using temporal aspects takes place in situation recognition, but also when states with unsolved interpretations are found. An admissible waiting time is then defined, either ptedefined, or on-line determined (prediction of the next change instant of the concerned variable). If one or several events occur during this waiting time which allows one to reduce the list of possible causes to a unique element, the problem is solved. Otherwise, the problem is recorded.

actions are proposed - if necessary - to the human operator. A biotechnological process being an hereditary system, the reasoning must be performed, in real-time, on the correlation between the different evolution profiles rather than on direct measurements, which are not sufficient to represent the fermentation state. So let us consider the causal graph associated with our application (fig. 1). The five kinds of signals previously described are shown. From the process measurements, a quantity state is defined (numerical-symbolic interface). Its vocabulary owns a finite number of elements, which allows one to use qualitative calculation techniques.

3. FERMENTATION PROCESS In order to establish the causal graph of influences, the fedbatch fermentation process considered is first described briefly . Generally, the aim of a fermentation process is to increase the concentration of biomass (X) in order to obtain the maximum concentration of product (P) through the use of a suitable feeding substrate (S) and oxygen (02). In this study, the culture of genetically engineered Escherichia Co/i was carried out to produce heterologous proteins. We are concerned with the micro-organism growth phase preceding the phase of protein excretion. The working criterion is defined as the maximization of the final product yield; that is, a maximum of biomass must be obtained (quantitative criterion) in the best possible state (maximization of the microorganism’s potential, i.e. qualitative criterion). The control goal is hence to act on the environment and carbon substrate feeding conditions in order to maintain the specific growth rate high and stable, then to detect the best induction time for proteins excretion. The environment variables are regulated at constant setpoints (36.9”C for temperature, latm for pressure) or setpoints which can evolve inside intervals (2-10 ppm for PO2,6.5-6.7 for pH). The action variables affecting oxygen transfer are the aeration flow rare, the stirrer speed, the percentage of entering oxygen and the reactor pressure. The feeding substrate flow rate is an action variable controlled by PO2 because substrate concentration cannot be measured on-line. Signals are compatible and accessible through a RS232 line. The temperature, PO2, pH, pressure, stirrer speed, introduced oxygen percentage and total added substrate mass can be measured in real-time.

4. QUALITATIVE MODELING The qualitative modeling is based on the influences between variables and their evolutionary causes. A symbolic meaning must then be associated on-line to the signal evolutions. They are interpreted and

Fig. 1.

Graph of causal influences of a fedbatch fermentation process with aeration

A five-value space has been chosen, in order to maintain a representation acute enough without an explosion of associated combinations. This state in. 13.

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Ten qualitative operators have been defined, based on this vocabulary, and corresponding to edges of the causal graph. opl" The "past" operator takes into consideration the variable's evolution (between previous and present values) in order to determine its true qualitative value. op2: The "resulting action" operator considers the case when two actions are simultaneously executed on the same variable. op3: The "waited trend" operator determines the apriori response of a variable. op4: The "P02 evolution prediction" operator is calculated from the oxygen consumption at previous and present instants. opS: The "remaining quantity" operator establishes the difference between the supply and consumption of a substance. op6: The "quantity prediction" operator allows short time prediction of the evolution of a quantity according to its value and evolution trend. op7: The "supplied quantities influence" operator takes into account the influence on the micro-organisms of accumulated supplies of several substances. op8: The "produced quantities influence" operator takes into account, like the preceding one, the influence on the micro-organisms of products formed by the reaction. op9: The "control of influences on growth" operator expresses the link between the microorganisms' growth rate and the rate of the slower internal process. opl0: Finally, the "consumption rate" operator takes into account the fermentation phase (several phases during a fermentation: latency, adaptation. exponential, slowing down and stop phases) in order to predict the consumption of a substance according to the biomass concentration estimation. As an example, op7 is presented in Fig. 2, and all the operators are detailed in (Steyer, 1991). opt pp p m f ff .-, quanti~suppliedQ2 PP___ _.PP__ LP_P___LP_P____P_P__i..P.P.... p pp p m p pp m..... pp m ' ff f _p_p____i f pp p f p pp ff pp DO ! p p [ p p *~ quantity supplied QI

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5. EXPERT SYSTEM The expert system methodology is particularly appropriate to implement the qualitative physics tools previously described. This is why a real-time Expert System for the control of a fedbatch fermentation process, BIOTECH, has been designed (Steyer and others, 1991a). Its goals are on the one hand to make a diagnosis of problems that appear, both from material and biological viewpoints, and, on the other hand, to control the process from the chronological accounts of fermentation events and from the predictions which can be made about the system's evolution. The basic idea and the main characteristic of an expert system comes from its architecture which consists of a separation between knowledge (rule base and fact base) and the mechanism using it (inference engine). An expert system generator referred to as KHEOPS has been designed in our laboratory (Ghallab and Philippe, 1988), and is used in this study. It is based on propositional monotonic logic with an integrated control level compiler. It is particularly well-suited for stipulating diagnostic procedures or general decisions. An application can thus be developed by employing the flexibility of enunciative

'BIOTECH programming and can then be exploited after compilation, with the efficiency of a procedural representation. This compilation enables the Expert System to make decisions within a very short bounded time. Human operator

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satisfaction judgement on the basis of the ~ance index. To date, nearly 650 rules have been established, especially with respect to the dissolved oxygen and pH. They deal with 117 facts (i.e. symbolic variables). They have the following distribution: 71 for numerical / symbolic transformation, o 47 for detection of actions, o 102 for management of regulated variables (pH, Temperature, Pressure), 73 for management of controlled variables (substrate), o 295 for managment of PO2 evolution, o 27 associated to the dialogue with the human operator, O 34 for choice of actions. Examples of rules are described in (Steyer and others, 1991b, 1991c). Several simulations (on a knowledge mathematical model which is more accurate than an action model), as well as twelve real-life experiments, have been carried out. BIOTECH has reacted in a globally positive manner as experimental conditions have been taken into account (Steyer and others, 1991b). In each case, BIOTECH has obtained: o accurate conclusions about the fermentation state, since the expert system associated the same meaning with signal evolutions as the expert operators did, o a choice of actions corresponding to those identified by the experts, ~> a time delay (longest being 0.06 s) and data acquisition delay (0.4 s on average) consistent with the real time process requirements.

Fig. 4: Architecture of BIOTECH Figure 4 shows the vertical architecture of the rule base of BIOTECH, such that it can reproduce the human expert manner of thinking. The stages of the signal analysis can be seen on this figure: o association of symbolic data to numerical information, definition of the macroscopic state from the study of measured variables, o research of the causes accountable for this state from analysis of the process' chronological account, propagation of the causes in the graph in order to retrieve the process' microscopic state, and management of clashes, determination of actions by propagation in the graph of their potential results, according to a

An essential part of BIOTECH deals with the operator interface, developed with AIDA software, an environment for the development of graphical interfaces, marketed by ILOG. Indeed, the function of the interface is really important for the integration of automatic systems for process monitoring. The BIOTECH interface is presented in Fig. 5 and Fig.6, as a synopsis of the fermentation process, with actual measurements and evolution curves of essential process variables. The human operator can see the complete fermentation by glancing at to the interfaces. When an event occurs, the operator can click the mouse to access BIOTECH conclusions. 6. CONCLUSION The goal of this paper was to show the advantages of Qualitative Physics for real-time help of fermentation process control. Tools have been developed which have been integrated in the Expert System BIOTECH, oriented for monitoring and control of fedbatch fermentation processes. A structure of causal graph type has then been used for management of the influences between the process variables. It allowed the cause-to-result

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BIOTECH connections in the physical system to be modelled. The interpretation of the physical state has been obtained, thanks to a reasoning on the standard state. Simulations and real-life experiments have been carried out with BIOTECH, which showed the advantages of employing the rule-based systems to address issues usually handled by human operators. This methodology has several fields of interest. It allows: o on-line monitoring of a fedbatch fermentation with the objective of functioning in the same way as the experts do, simulation and explanation of the fermentation with eventually a teaching goal, O "intelligent" study of past fermentation recordings in order to enhance the laboratory "memory". A long-term system could be developed that would be easily updated and capable of addressing all issues from the cell design to the process control and downstream processes. 7. REFERENCES Asama H., T. Nagamune, M. Hirata, A. Hirata and I. Endo (1988). An expert system for diagnosing fermentation processes. Eng. Found. Conf., Santa Barbara (California, USA). Bastin G. and D. Dochain (1985). Stable adaptive controllers for waste treatment by anaerobic digestion. Env. Techn. Letters, 6,584-593. Cooney C.L., G.M. O'Connor and F. SanchezRiera (1988). An expert system for intelligent supervisory control of fermentation process. 8 th International Biotechnology Symposium, Paris (France). Ghallab M. and H. Philippe (1988). A compiler for real time knowledge based systems. IEEE International Syrup. on AI for Industrial Applications, Hitachi City (Japan), pp 287293. Guerrin F. (1990). Valorisation aquacole d'eaux us6es trait6es par lagunage naturel, 6valuation biotechnique et mod61isation des connaissances. Doctor Thesis of Univ. Paul Sabatier, Toulouse (France). Hong J. (1986). Optimal substrate policy for a fedbatch fermentation with substrate and product inhibition kinetics. Biotechnol. Bioeng., 28, 1421-1431. Ohno H., E. Nakanishi and T. Takamatsu (1976). Optimal control of a semibatch fermentation. Biotechnol. Bioeng., 18, 847-864. Queinnec I., B. Dahhou B. and M. M'Saad (1991). On adaptive control of fedbatch fermentation processes. ECCgl, Grenoble (France), pp 1636-1641. Steyer J.Ph. (1991). Sur une approche qualitative des syst~mes physiques: aide en temps r6el .~ la conduite de proc6d6s fermentaires. Doctor Thesis of Univ. Paul Sabatier, Toulouse

(France).

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Steyer J.Ph., J.L. Uribelarrea, D. Simoes and J.B. Pourciel (1991a). A real time expert system for the control of fedbatch fermentations. Mathematical and Intelligent modeling in System Simulation, Scientific Publishing, pp. 767-772. Steyer J.Ph., J.B. Pourciel, D. Simoes and J.L. Uribelarrea (1991b). On line biotechnological process control by means of real time expert system. ECC91, Grenoble (France), 12321235. Steyer-J.Ph., J.B. Pourciel, D. Simoes and J.L. Uribelarrea (1991c). Qualitative knowledge modeling used in a real time expert system for biotechnological process control. IMACS International Workshop on Qualitative Reasoning and Decision Support Systems, Toulouse (France).