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Application of Control Engineering to Fermentation Processes
CHEMICAL PROCESS CONTROL II
APPLICATION OF CONTROL ENGINEERING TO FERMENTATION PROCESSES A. Halme* and A. Holmberg** *The University of Oulu, Division of Control Engineering, Oulu, Finland **Helsinki University of Technology, Systems Theory Laboratory, Helsinki, Finland
Abstract. The paper deals with applications of control engineering methods to fermentation processes. The progress, difficulties and role of control engineering in this quite new field are discussed according to the authors' experience and opinions. A brief survey on dynamic analysis and modelling of fermentations is presented. Reasonable objectives in control and optimization are considered. As a case example computer control of a newly developed fungal protein process is considered. Keywords. Biocontrol; fermentation processes; modelling; control engineering computer applications; food processing industry. INTRODUCTION
sludge process utilized already over 100 years in communal wastewater treatment has become sufficiently understood only in the last ten years so that reasonable process control and automation are possible. Modern biotechnology has, however, made recently much progress in process understanding and at the same time when new processes are developed, know-how of biochemical reactions and process dynamics have been improved considerably. This together with recent progress in measurement and instrumentation technology has created a good basis for application of control engineering and automation to fermen tation processes. At present there exist a few computer controlled f er mentation processes in industry and several development projects are going on.
Biotechnological process industry is a new field for control engineering and computer applications. Partly this is because these processes have only quite recently attained greater economic significance in process industry and partly because of the extraordinary nature of the processes, which makes them quite different than ordinary chemical processes. Modern biotechnology is, however, probably one of the most rapidly developing fields of the process industry in the near future. Besides classical beavery and yeast products the modern biotechnology is capable to produce a large variety of products as organic acids, antibiotics, steroids, enzymes and proteins by fermenting a suitable string of micro-organisms in suitable substrates. Besides production processes in food and drug industry biological processes are widely used in waste treatment, which is also a rapidly jeveloping field.
The purpose of this paper is not in the first place to be a survey on control engineering applications in fermentation processes, but more to express the authors' opinions and ex periences on the subject. The paper is divided into three parts. First an introduction to fermentation process e s and the role of control engineer ing in these processes is made. Then process analysis and modelling are briefly considered. The purpo se is to show that also fermentation processes can be "got in hand" for reasonable process control and optimization. Finally an application is considered. It deals with is a newly developed fungal single-cell protein process, called Pekilo-process. Computer
rhe heart of a biotechnological process is fermentation, where biochemical reactions take place . The reactions are based on metabolism of living cells and differ considerably from ordinary chemical reactions. Due to the presence of living organisms there are many special features in ~oth process technology and control =ngineering. Conventional biotechnology, such as beer making, has been (and still is) more "art" than technology, and e.g. the activated 291
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control system under development for this process is discussed. FERMENTATION AND FERMENTORS Fermentation is classically understood as a biochemical process, where organic compounds (substrates) react via the metabolism of living microorganisms. For technical purposes fermentation is used like chemical processes to produce new products or to convert feed substances to other, more feasible forms. The main application fields are food industry, drug industry and waste treatment. Such products as beer, baker's yeast, enzymes, protein, antibiotics and steroids are examples of product in the first cases. Fermentation can be also used to obtain different kind of hydrolysis products e.g. from cellulose. In waste-water treatment biological processes such as activated sludge, trickling filters and sludge digestion are widely utilized. The purpose in these cases is to use micro-organisms to remove dissolved organic compounds from water. A fact, which often makes fermentation processes economically reasonable is the amazing growth rate of microbes. The doubling time of biomass may be only 2-3 hours or even less. From the process engineering point of view fermentation can be considered as an autocatalytic like reaction. The cultivated micro-organisms are in the position of catalyst and the feed substances include the substances needed in the metabolism. The reaction can be aerobic, anaerobic or photosynthetic. Most industrial production processes are aerobic, anaerobic processes are used in quite
few cases, e.g. in alcohol production and sludqe digestion, and photosynthetic only when cultivating algae in some cases. In each case the products are biomass, metabolites and heat. The substrate liquid, additional nutrients needed and air in aerobic cases are fed to a vessel called fermentor, where the micro-organism growth takes place. The substrate liquid includes a carbon source, usually suitable carbohydrates or hydrocarbon compounds such as sugars, methane etc. The nutrients usually needed are nitrogen, phosphorus, potassium and certain vitamins. The emulsion formed by liquid, micro-organisms and air is usually strongly agitated to obtain good mass transfer properties. Depending on the process in hand certain pre and secondary treatments are also needed. Fig. 1 shows the simplified flow diagram of an industrial protein fermentation process, considered in more detail later on. As to the operating principle fermentations can be batch, continuous or semi continuous type. The choice of the operating principle depends on the properties of the growth process and especially the product formation mechanism. Many processes, however, can be operated in all these modes. The tendency in modern biotechnology is, no doubt, towards continuous or semicontinuous processes, which are often more economic than batch processes. The products are usually classified into two categories depending on whether they are formed inside the cell, so-called intracellular products, or are secreted into the growth medium, so-called extracellular products. In the case of intracellular products (e.g. protein) the biomass production is important, but in extracellular products (such as many en-
fft'l'Mnter
sp.nt liq uor - .............--4 from pulp mill
Fig. 1. Simplified schema of a protein fermentation process
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Fermentation Processes
zymes) it is only of secondary importance. The biomass may be even a problematic waste of the process as it is in antibiotic production or in activated sludge method. The constructions of fermentors differ mainly in mixing and aeration systems. In many cases, especially when high growth rates are maintained, mass transfer of oxygen is a limiting factor. Most industrial fermentors have the complete mixing character but also tube and bed-type fermentors are used. Another important technical point is asepticity, as to which the demands vary depending on the process. Strict asepticity is demanded in such cases where the cultivation must be kept free from other organisms than those cultivated or in the cases where the cultivated organisms are dangerous to the environment. In many cases the string of microorganisms used in a specific process has been developed via mutation from a natural string and is able to live only in aseptic conditions. To maintain the aseptecity is often one of the most difficult problems in fermentation technology and is a continuous challenge for process engineers in this field. ROLE OF CONTROL ENGINEERING IN BIOTECHNOLOGY Fermentation is quite a new field for control engineers. However, during the last ten years considerable progress has taken place and even computer control is feasible technology in this field today, see e.g. Nyiri (1972), Jefferis III (1975, 1976), Swartz and others (1976). Generally taken control has an important role in fermentation processes. To maintain acceptable environmental conditions in a fermentor it is mostly necessary to have certain basic control loops such as flow, temperature, pH, level and pressure. In addition to that, production control is often profitable, because fermentations are usually sensitive to different kinds of disturbances, which may easily change production conditions. Such disturbances can originate in environmental conditions, such as lack of certain nutrient or unsuitable pH, or from the organism itself, e.g. from string genetic modification. The disturbances may be long lasting because of long time constants related to the biological growth process. In the worst case the process must be shut down and started again by inoculation. Restarting a plant may take several days. Energy savings may be also considerable. For example in the activated sludge process the aeration
rate can be controlled according to the load variations, which saves 15-20 % electrical energy. The control policies which maintain optimum production conditions in different situations thus often result in much better production profit compared to the uncontrolled case. However, in applications there exist serious difficulties, which are related to instrumentation on one hand and sufficient knowledge of process behaviour on the other hand. Many essential process variables, such as biomass and substrate concentrations, are often impossible to measure with reliable on-line instruments. This means that qUite few such variables which are closely related to the biochemical process can be directly measured. Regular and expensive laboratory analysis are often needed in process monitoring. Process behaviours are in many cases complicated, especially this concerns the cases where secondary metabolities are produced. On the other hand fermentation processes have usually long time constants (5-20 h) and some predictive models are necessary if reasonable process controls are wanted. For this reason process modelling is of considerable interest and is presently an intensively studied field in biotechnology. In many cases, especially when a pure string of microorganism is cUltivated and a growth related product is considered, the advanced models fit amazingly well. However, in other cases validity of the models may be poor and in some cases it has turned out impossible to present a model for the process (e.g. beer making is a good example of this). In the next chapter process analysis and modelling are considered in more detail. ANALYSIS AND MODELLING OF FERMENTATIONS The functioning of a fermentation process is based on enzymatic reactions related to cell metabolism. The number of interactive reactions may be several hundred and in most cases not very precisely known. For practical control purposes there is usually no sense to begin the model building on this basis. On the other hand neither the complete blackbox approach is fruitful, because it does not explain the course of the main biochemical actions in the process. Understanding and monitoring of these actions are often necessary to get reasonable control. The dynamic model may also turn out useful in the design state of the fermentation process, and a blackbox model can not be
A. Halme and A. Holmberg
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used for this purpose. The way commonly chosen is to model the main material and energy balances by considering the cells in the fermentor as a uniform culture, or if required by characterizing the cell mass by suitable distributions e.g. the age distribution. Detailed modelling of mixing circumstances may also be important especially in large scale fermentors. The following actions form usually the basis for the model building: .growth of biomass .assimilation of substrates and nutrients .mass transfer and consumption of oxygen (in airobic processes) • effect of environmental conditions • heat production and transfer In addition to these actions the formation of the product(s) should be usually modelled. This may be an easy or difficult task depending on the product. The products can be classified into two groups, "growth associated" and "nongrowth associated". The products in the first group are normally intracellular, such as protein, and the production rate can be directly related to the growth rate. The second group products may be extracellular or intracellular and usually they are so-called secondary metabolites, such as antibiotics and toxins. Because in this case the product formation is usually not related to any single process variable it is often difficult to obtain a reasonable model. In fact tbe Droduct formation is dependent on the biological states of individual cells. A way to describe this state is the cell age, and the product formation may be related to the age distribution of the culture. There exists quite an extensive literature on the analysis and modelling of fermentations, see e.g. the books by Aiba and others (1973), Atkinson (1974), Pirt (1976), and review articles by Khosrovi and Topiwala (1974) and Yoshida and Taguchi (1976). The biomass growth is usually modelled by using the so-called Monod model (Monod 1950) or its modifications. In the Monod model the growth rate is described by the equation G = I1X,
unit time. The specific growth rate depends on the growth limiting substrate, which usually is the carbon source, by the Michaelis-Menten form I1m S = ~ ,
l1(s)
(2)
s
where s is the concentration of the limiting substrate in the growth medium, 11 is the maximum specific growth ra~e and k is the MichaelisMenten coefficien~. The Monod model can be derived from enzyme kinetics (see e.g. Pirt, 1975). The name originates from the fact that Monod first proved its validity experimentally in microbe cultures. Since then the Monod model has been modified and improved in many ways to consider for example substrate inhibition (Andrews, 1971) and multisubstrate limiting e.g . in the case of carbon and oxygen (Sinclair and Ryder, 1975). Also other type models have been investigated (a survey see e.g. Yoshida and Taguchi, 1977). The effect of environmental factors, such as temperature and pH, may be modelled by considering their effect on the specific growth rate 11 (see e.g. Topiwala and Sinclair, 1971; Andrews, 1971). The effect of temperature seems to be quite simple and is similar to different microbes, although the feasible range of temperature varies. The effect of pH is more complicated and may also have other consequences than changes in the specific growth rate. Both variables usually have optimum values, which may be quite accurate. Utilization of substrates and nutrients is usually described by yield or consumption coefficients. For example it is commonly assumed that a certain amount of substrate consumed produces a certain amount of biomass L'lX
=
( 3)
-Y L'l S,
where Y is the substrate yield coefficient. The substrate consumption in a unit volume is directly related to the growth rate SCON
= Y1 G
•
( 4)
The yield coefficient Y is usually supposed to be a constant, but it may v ary if the environmental conditions vary to a great extent. In the case where the carbon and energy sources are the same substrate its consumption can be divided into three parts
(1 )
(5) where 11 is the so-called specific growth rate and X the biomass density. The equation (1) gives the amount of biomass produced per unit volume per
where L'l S c = substrate needed for cell carbon
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Fermentation Processes
6S EG = substrate needed for growth energy 6S EM= substrate needed for maintenance energy The first two terms are directly related to growth but the third one to the amount of biomass. A more accurate representation for the substrate consumption may thus be obtained from the equation SCON
1 =Y G+mX,
(6)
G
where (7)
and m is the maintenance coefficient. The yield coefficient Y is a consG coefficient tant but the maintenance m may vary e.g. according to the age of cells. The mass transfer of oxygen takes place from the air bubbles to the growth liquid by diffusion process. The microbes are usually able to respirate only dissolved oxygen. The mass transfer to a unit volume of liquid can be described by the equation
(8)
and part of it is released as heat. Also in protein synthesis the chemical energy is not utilized as 100 % efficiency but part of it is released as heat. In practice the heat production is usually linearly related to the oxy gen consumption (see e.g. Imanaka and Aiba, 1976). The total heat balance of a fermentor depends also on other factors, such as the agitation energy, heat input and output via the air flow and other flows, and h e at transfer in heat exhangers. A dynamic model for a continuous fermentation proc e ss, when complete mixing is supported, can be written as follows: Biomass: "' =
( p (S,C)-kD)X-DX,
p (S,C)
=
Pm KS+S'KC+C s c
(10) (11)
Substrate:
s=
(12)
Dissolved oxygen: C = K a(C*-C)-DC-0 2CON L
(13)
Oxygen consumption: 1
02CON = ay p (S,C)X+bX
(14)
Heat:
C*
transfer coefficient present dissolved oxygen concentration (or partial pressur~ saturated dissolved oxygen concentration (or partial pressur~
The transfer coefficient K a depends on the aeration rate and a~itation powers. Additional substances such as antifoam agents may also have an effect on its value. The oxygen consumption can be usually modelled by the equation
( 15) The growth is limited both by substrate and by oxygen. The following notations have been used besides those defined before dilution rate = liquor flow rat~ D emulsion volume influent substrate concentration death coefficient Michael-Menten coefficient fermentor temperature
(9)
which is of the same type as eq. (6). Also in this case the first term descibes the oxygen needed for the growth energy and the second term the oxygen for the maintenance energy. Usually the coefficient a is quite constant but b may vary e.g. according to the cell age. Also other types of oxy gen transfer and consumption models have been suggested. An interesting theory is the direct adsorption mechanism described by Mimura and co-workers (1973) and Halme and co-workers (1977a~. The heat production is related to the energy generation in cell metabolism. Part of the energy obtained in the substrate dissociation process is accumulated as chemical energy (ATP)
cooling fluid temperature _"_"- flow
K
heat transfer area of the coolirB coil (scaled) proportional constant
As is seen from eq . (10)-(15) the fermentation process is quite highly nonlinear. Partly due to this fact and partly because measurement data may be quite uncertain the identification and parameter estimation is often a tedious and time consuming task. Several general identification methods have been tested and special algorithms developed fot fermentation processes. For example extended Kalman filter has been studied by Svrcek and co-workers (1974), a non-
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A. Halme and A. Holmberg
linear least square algorithm by Nihtila and Virkkunen (1977) and a special type of polynomial method by Ribot (1976). An advanced computer system for on-line calculations has been described by Reuss and co-workers (1976). A method found very useful and practical by the authors is the use of sensitivity analysis (D'Ans and co-workers, 1972; Gyllenberg and co-workers, 1975). In the method parameter sensitivity functions are used to help parameter adjusting and to demonstrate the meaning of parameter changes. CONTROL AND OPTIMIZATION Stabilizing Control and Process Monitoring Biological processes are usually open loop stable with respect to the important variables and they work somehow with hardly any control. Thus there exist no principal problems in stabilizing control. However, practical problems related to process monitoring and instrumentation may be considerable. In spite of recent progress in measurement technology, many important variables cannot yet be measured by any automatic means. For example the photo-optical measurement of biomass density, which is the only way used so far, succeeds in quite rare cases. Due to circumstances even such an ordinary measurement as the emulsion volume (level) in a fermentor is often difficult to realise with the accuracy. Sensors like pH, redox, dissolved oxygen should be chosen carefully to guarantee reliable operation. This is especially the case in waste-water treatment processes, where circumstances are hard (see e.g. Aarinen and co-workers, 1978). In aseptic fermentations a special problem may also be due to the fact that sensors have to be changable without losing asepticity. The number of hand measurements is usually high because autoanalyzers are expensive and difficult to use e~ pecially in broths including cells and other solids. On the other hand gas analyzers, such as O - and CO 2 2 analyzers, and gas chromatographs are useful and commonly used devices in many fermentation processes. Besides measuring mass balances, such as oxygen transfer, the information obtained from exhaust gases may be used for indirect measurement or estimation of important process variables e.g. biomass growth rate and concentration (see e.g. Zabriskie and co-workers, 1976; Halme and co-workers, 1977b). A corresponding measurement is also heat production and balance, which can give important indirect in-
formation. The use of estimation techniques provides, however, good process know-how and accurate models. Optimization and Production Control In continuous fermentation processes steady state optimization as usually profitable. In Fig. 2. there is a typical set of curves, in which the biomass concentration, the biomass growth rate and the substrate concentration in a continuous fermentor are shown as functions of the dilution rate the other variables being constant. As is clearly seen the growth rate has the maximum at certain dilution rate. If the product is growth associated (as in this case is) this gives the pOint of maximum production. Economically, however, this need not be the best point to operate, because the consumption of raw materials, e.g. substrate and nutrients, increases with increasing dilution rate. In production fermentations the optimization criterion is usually the net earning, which is to be maximized. If the process is used for waste treatment the "product", e.g. clean water, has usually no~ a price and the criterion may be to attain a gi v en waste removal percentage with minimum operational costs. The best operation point is then usually another than if the same process is used for production. Depending on the process the variables which can be used for optimization are dilution rate, inlet substrate concentration, nutrient feedings, pH, temperature, aeration and agitation powers, and organism or substrate recirculation. A typical continuous industrial fermentation process is protein production, of which an example is given in the next chapter.
Fig. 2. Steady state behaviour of the continuous fermentation process shown in fig. 1. Biomass (X), substrate (S) concentrarions and growth rate (G) as the function of dilution rate (D).
297
Fermentation Processes
In batch fermentations the process is in transient state during the whole operational interval. In optimization the transient is tried to be controlled so that the average net earning is maximized. Such variables as temperature, pH and aeration and agitation powers can be used to control the transient. The batch time is an important optimized variable. It should usually include also the preparation time for the next batch. The preparation time may be relatively long because of cleaning and sterilization operations. A time lag between innoculation and start of growth may be also considerable and should be taken into account. Such difficulties do not appear in continuous operation. An example of industrial processes, which are mostly of batch type are penicillin fermentations. A main reason for batch operation is that the organisms used are sensitive to mutation and may be degenerate after a limited number of doublings. In such circumstances continuous operation is not possible. A way to improve the operation of batch process is a semi-continuous operation. Periodical draw-off or "fed batch" operations (see e.g. Pirt, 1974) are especially suitable for fermentation processes, because the preparation time between batches disappears and even average ~roduct ion may be better than in batch operation. Semi-continuous operation is in fact a type of periodic continuous operation by which optimality of continuous processes may be improved in certain conditions (see e.g. the review by Baile y , 1973). A nice application of this idea in an amino acid fermentation have been presented by Ohno and co-workers (1976). Dynamic optimal control can be used when controling a fermentation process from an operation state to another. Situations in which this may be significant are e.g. start-up procedure, batch control, and change of the operation state during continuous operation. Reasonable criteria are e.g. minimum time, maximal production or minimum cost profit. The problem of controlling a continuous process during transient stages have been studied among others by d'Ans and co-workers (1972), Muzychenko and co-workers (1974) and Takamatsu and co-workers (1974). King and coworkers (1974) have studied the control of a penicillin batch fermentation by using temperature as the control variable. The problems solved have usually lead to bang-bang or singular control policies. In prac-
tice such controls may have the disadvantage that they cause shocks to the microbes, which in turn may disturb the process considerably. However, a feasible suboptimal control ? olicy can often be easily found. CASE EXAMPLE: A PROTEIN PROCESS Single-cell protein (SCP) production for animal or human food is an interesting application of fermentation processes. Several processes have been developed for that purpose, which utilize different substrates and different micro-organisms. An example is the Pekilo-process recently developed in Finland (Forss and co-workers, 1974). At the moment there exis~one production plant in Jamsankoski, Finland. It utilizes sulphite pulping spent liquor and has the capacity of 10.000 tn per year protein, which is used for anima~ food. Quite extensive system eng~n eering and control studies have been done for this process by the authors and co-workers during the last few years and also a computer control system has been desisned for t~e . process. Following short descr~pt~on concerns these studies reported in more detail elsewhere (Halme and coworkers, 1977 a,b). Description of the Pekilo-process In the Pekilo-process SCP for feed purposes is produced from a suitable substrate containing carbohydrates by fermenting a string of paeci~omy ces varioti micro fungi in asept~c conditions. When sulphite pulping spent liquor is used as the su~stra te the process includes the un~t operations shown in the simplified schema in Fig. 1. In the primary treatment S02-content of the liquor is lowered by steam stripping. At the same time liquor is also sterilized, i.e. possible strange organisms are killed. After stripping the liquor is cooled and fed to the fer-
Fig. 3. Microscopic photograph of Paecilomyces varioti microfungl.
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A. Halme and A. Holmberg
mentor, where the microfungi mycelium is grown. A microscopic picture of the mycelium is shown in Fig. 3. Fermentation takes place continuously in one stage without organism or substrate recirculation. Detention time is 3-4 h, pH 4,5-4,7 and temperature 38-39 0 C. The fermentation is aerobic and besides oxygen also nitrogen, phosphorus and potassium are needed as additional nutrients. Oxygen is fed to the fermentor through bubble aerators, nitrogen as ammonia in connection with pH-control. Phosphorus is fed as phosphorus acid (P205) and potassium as potassium chloride (KC1) into the liquid stream in the stripper unit. The process product is mycelium, which is first separated from the fermentor effluent by filtering, then washed (washing takes place in the filter unit), after that dried, first mechanically and then by heat. Finally the product may yet be pressed to pills or some other form. The raw protein content of the product is high, 55-60% w/w, and it can be used as a protein source for animals (in principle also for man) as it is or in feed mixtures. The Jamsankoski production plant includes two parallel strippers and two fermentors, 360 m3 each, and a so-called inoculation line for start up of the process which consists of a 15 m3 inoculation fermentor and a 150 1 laboratory fermentor. !-lore details from the Pekiloprocess can be found in references (Forss and co-workers, 1974; Romantzuk, 1974). Instrumentation and process operation have been described by Halme and co-workers (1977 b) . Process AnalYSis and Computer Control Extensive analysis and modelling studies of the Pekilo fermentation have been done to improve the process design on the one hand and control on the other hand. A dynamic model constructed for control purposes is based on Monod growth model, a direct adsorption for the oxygen transfer and modelling of the cell age distribution. The latter is used to model variations in protein content and oxygen yield coefficient. This makes it possible to estimate the growth rate from the oxygen consumption. The validity of the dynamic model is amazingly good. Details can be found in references (Halme and co-workers, 1977 a,b). The model is utilized in the computer control system developed to improve monitoring and control of the process. A preliminary study of the computer system has been reported previously by Halme and co-workers (1977 b). The
computer system installed now in the Jamsankoski plant does the usual tasks of a process computer system: data acquisition, analysis, storing and process control actions. Higher level controls are related to e.g. such actions as nutrient and antifoam agent addition, aeration rate and agitator speed control, and growth rate control. Special features of the system are e.g. the high number of hand measurements, high data storing requirements and quite a large analYSis program. Process monitoring and control are done via CRT displays and by using a specially designed operator keyboard. The reporting system gives shift, day, week and month reports. The basic system is similar to that described by Meskanen (1976). The hardware consists of a main computer (POP 11/34), a subcomputer (POP 11/04), two disks, two CRT-displays and two typewriters. The system has been installed first for experimentation in the inoculation line of the plant and will be later enlarged to the production line. CONCLUSION The authors' opinion is that the application of control engineering to fermentation processes is an interesting and challenging new field. Pioneering work has already been done and in future the number of applications will probably rapidly increase with increasing importance of these processes. It is comforting for control engineers to know that many fermentation processes behave in an understandable way and can be controlled even by computers. REFERENCES Nyiri, L.K. (1972). Application of Computers in Biochemical Engineering. Advances in Biochemical Engineering, Vol. 2, SpringerVerlag, Berlin. Jefferis Ill, R.P. (1975). Computers in the Fermentation Pilot Plant. Process Biochem., 10, 23. Jefferis Ill, R.P. (Ed.-)-(1976). Workshop Computer Applications in Fermentation Technology. Ve~ lag Chemie, Veinheim. Swartz, J., Wang, H., Cooney, C.L. and Wang, D.I.C. (1976). Computer-aided yeast production. Abstracts of papers of the fifth international fermentation symposium. Berlin. Aiba, S., Humphrey, A.E., Millis, N.F. (1973). Biochemical Engineering, 2nd ed. Academic Press, New York.
Fermentation Processes
Atkinson, B. (1974). Biochemical Reactors. Pion Ltd, London. Pirt, S.J. (1975). Principles of Microbe and Cell Cultivation. Blackwell, Oxford. Khosrovi, B. and Topiwala, H.H. (1974) Mathematical Modelling and Computer Control in Fermentation Processes. Annual Report on the Progress of Applied Chemistry, 59, 357. Yoshida, T. and Taguchi, H. (1976). Use of Models in Fermentation Control. In R.P. Jefferis III (Ed.) Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. Monod, J. (1950). La technique des cultures continues; theorie et applications. Ann. Inst. Pasteur, 79, 390. Andrews, J.F. (1971). Kinetic models of biological waste treatment processes. Biotechnol. Bioeng. Symp. 2, 5. Topiwala, H. and Sinclair, C.G. (1971) Temperature Relationships in Continuous Culture. Biotechnol. Bioeng. 13, 795. Mimura, A., Takeda, I., Wasaka, R. (1973). Some Characteristic Phenomena of Oxygen Transfer in Hydrocarbon Fermentation. Biotechnol. Bioeng. Symp. 4. Halme, A., Holmberg, A. and Tiussa, E. (1977a). Modelling and Control of a Protein Fermentation Process Utilizing the Spent Sulphite Liquor. Proc. IFAC Symposium on Environmental Systems Planning, Design and Control, Pergamon, Oxford. Imanaka, T. and Aiba, S. (1976). A Convenient Method to Estimate the Rate of Heat Evolution in Fermentation. J. Appl. Chem. Biotechnol. ~, 557. Svrcek, W.Y., Elliott, R.F. and Zajic, J.E. (1974). The Extended Kalman Filter Applied to a Continuous Culture Model. Biotechnolog. Bioeng.16, 827. Nihtila, M. and Virkkunen, J. (1977). Modelling, identification and program development for a pilotscale fermentor. 5th IFAC/IFIP Internarional Conference on Digital Computer Applications to Process Control, North-Holland, Amsterdam. Ribot, D. (1976). Identification of Fermentations by Polynomial Methods. In R.P. Jefferis III (Ed.), Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. D'Ans, G., Gottlieb, D. and Kokotovic, P. (1972). Optimal control of bacterial growth. Automatica, 8, 729. Gyllenberg, A., Hamalainen, R.P~ and Halme, A. (1975). Modelling of Microbial Systems for Process Op-
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timization and Control. Report B 26, Systems Theory Laboratory, Helsinki University of Technology. Aarinen, R., Tirkkonen, J., Halme, A. (1978). Experiences on Instrumentation and Control of Activated Sludge Plants - a Microprocessor Application. 7th IFAC Triennal World Congress, Helsinki. Zabriskie, P.W., Armiger, W.B. and Humphrey, A.E. (1976). Application of Computers to the Indirect measurement of Biomass Concentration and Growth Rate by Component Balancing. In R.P. Jefferis III (Ed.), Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. ~euss, M., Jefferis Ill, R.P. and Lehman, J. (1976). Application of an On-line System of Coupled Computers to Fermentation Modelling. Ibib. Halme, A., Kiviranta, H., Kiviranta, M. (1977b). Study of a SingleCell Protein Fermentation Process for Computer Control. Proc. IFAC/ IFIP 5th International Conference on Digital Computer Applications to Process Control, North Holland Amsterdam. Pirt, J. (1974). The theory of fed batch culture with reference to the penicillin fermentation. ~. Appl. Chem. Biotechnol. 24, 415. Bailey, J.E. (1973). Periodic Operation of Chemical Reactors: A Review. Chem. Eng. Commun, 1, Ill. Ohno, H., Nakanishi, E. and Takamatsu, T. (1976). Optimal Control of a Semibatch Fermentation. Biotechnolo Bioeng., 18, 847. Murechenko, L.A., Mascheva, L.A. and Yakovleva, G.V. (1974). Algorithm for optimal control of microbiological synthesis. Biotechnol. Bioeng. Symp. 4. Advances in Microbial Engineering, ~, 629. Takamatsu, T., Hashimoto, I., Shionya, S., Mizuhara, K., Koihe, T. and Ohno, H. (1975). Theory and practice of optimal control in continuous fermentation process. Automatica, 11, 141. King, R.E., Aragona, J. and Constantinides, A. (1974). Specific optimal control of a batch fermentor. Int. J. Control, 20,869. Forss, K., Passinen, K. an~Sjostrom, E. (1974). Proc. Symp. Wood Chemistry, Pure and Applied, ACS meeting, Los Angeles. Romantchuk, H. (1974). The Pekiloprocess. Single Cell Protein, MIT Press. Meskanen, A. (1976). Design of the Man-Machine Interface for Computer Coupled Fermentation. In R.P. Jefferis III (Ed.) Workshop Computer Applications in Fermentation Technology, Verlag Chemie.
APPLICATION OF CONTROL ENGINEERING TO FERMENTATION PROCESSES A. Halme* and A. Holmberg** *The University of Oulu, Division of Control Engineering, Oulu, Finland **Helsinki University of Technology, Systems Theory Laboratory, Helsinki, Finland
Abstract. The paper deals with applications of control engineering methods to fermentation processes. The progress, difficulties and role of control engineering in this quite new field are discussed according to the authors' experience and opinions. A brief survey on dynamic analysis and modelling of fermentations is presented. Reasonable objectives in control and optimization are considered. As a case example computer control of a newly developed fungal protein process is considered. Keywords. Biocontrol; fermentation processes; modelling; control engineering computer applications; food processing industry. INTRODUCTION
sludge process utilized already over 100 years in communal wastewater treatment has become sufficiently understood only in the last ten years so that reasonable process control and automation are possible. Modern biotechnology has, however, made recently much progress in process understanding and at the same time when new processes are developed, know-how of biochemical reactions and process dynamics have been improved considerably. This together with recent progress in measurement and instrumentation technology has created a good basis for application of control engineering and automation to fermen tation processes. At present there exist a few computer controlled f er mentation processes in industry and several development projects are going on.
Biotechnological process industry is a new field for control engineering and computer applications. Partly this is because these processes have only quite recently attained greater economic significance in process industry and partly because of the extraordinary nature of the processes, which makes them quite different than ordinary chemical processes. Modern biotechnology is, however, probably one of the most rapidly developing fields of the process industry in the near future. Besides classical beavery and yeast products the modern biotechnology is capable to produce a large variety of products as organic acids, antibiotics, steroids, enzymes and proteins by fermenting a suitable string of micro-organisms in suitable substrates. Besides production processes in food and drug industry biological processes are widely used in waste treatment, which is also a rapidly jeveloping field.
The purpose of this paper is not in the first place to be a survey on control engineering applications in fermentation processes, but more to express the authors' opinions and ex periences on the subject. The paper is divided into three parts. First an introduction to fermentation process e s and the role of control engineer ing in these processes is made. Then process analysis and modelling are briefly considered. The purpo se is to show that also fermentation processes can be "got in hand" for reasonable process control and optimization. Finally an application is considered. It deals with is a newly developed fungal single-cell protein process, called Pekilo-process. Computer
rhe heart of a biotechnological process is fermentation, where biochemical reactions take place . The reactions are based on metabolism of living cells and differ considerably from ordinary chemical reactions. Due to the presence of living organisms there are many special features in ~oth process technology and control =ngineering. Conventional biotechnology, such as beer making, has been (and still is) more "art" than technology, and e.g. the activated 291
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control system under development for this process is discussed. FERMENTATION AND FERMENTORS Fermentation is classically understood as a biochemical process, where organic compounds (substrates) react via the metabolism of living microorganisms. For technical purposes fermentation is used like chemical processes to produce new products or to convert feed substances to other, more feasible forms. The main application fields are food industry, drug industry and waste treatment. Such products as beer, baker's yeast, enzymes, protein, antibiotics and steroids are examples of product in the first cases. Fermentation can be also used to obtain different kind of hydrolysis products e.g. from cellulose. In waste-water treatment biological processes such as activated sludge, trickling filters and sludge digestion are widely utilized. The purpose in these cases is to use micro-organisms to remove dissolved organic compounds from water. A fact, which often makes fermentation processes economically reasonable is the amazing growth rate of microbes. The doubling time of biomass may be only 2-3 hours or even less. From the process engineering point of view fermentation can be considered as an autocatalytic like reaction. The cultivated micro-organisms are in the position of catalyst and the feed substances include the substances needed in the metabolism. The reaction can be aerobic, anaerobic or photosynthetic. Most industrial production processes are aerobic, anaerobic processes are used in quite
few cases, e.g. in alcohol production and sludqe digestion, and photosynthetic only when cultivating algae in some cases. In each case the products are biomass, metabolites and heat. The substrate liquid, additional nutrients needed and air in aerobic cases are fed to a vessel called fermentor, where the micro-organism growth takes place. The substrate liquid includes a carbon source, usually suitable carbohydrates or hydrocarbon compounds such as sugars, methane etc. The nutrients usually needed are nitrogen, phosphorus, potassium and certain vitamins. The emulsion formed by liquid, micro-organisms and air is usually strongly agitated to obtain good mass transfer properties. Depending on the process in hand certain pre and secondary treatments are also needed. Fig. 1 shows the simplified flow diagram of an industrial protein fermentation process, considered in more detail later on. As to the operating principle fermentations can be batch, continuous or semi continuous type. The choice of the operating principle depends on the properties of the growth process and especially the product formation mechanism. Many processes, however, can be operated in all these modes. The tendency in modern biotechnology is, no doubt, towards continuous or semicontinuous processes, which are often more economic than batch processes. The products are usually classified into two categories depending on whether they are formed inside the cell, so-called intracellular products, or are secreted into the growth medium, so-called extracellular products. In the case of intracellular products (e.g. protein) the biomass production is important, but in extracellular products (such as many en-
fft'l'Mnter
sp.nt liq uor - .............--4 from pulp mill
Fig. 1. Simplified schema of a protein fermentation process
293
Fermentation Processes
zymes) it is only of secondary importance. The biomass may be even a problematic waste of the process as it is in antibiotic production or in activated sludge method. The constructions of fermentors differ mainly in mixing and aeration systems. In many cases, especially when high growth rates are maintained, mass transfer of oxygen is a limiting factor. Most industrial fermentors have the complete mixing character but also tube and bed-type fermentors are used. Another important technical point is asepticity, as to which the demands vary depending on the process. Strict asepticity is demanded in such cases where the cultivation must be kept free from other organisms than those cultivated or in the cases where the cultivated organisms are dangerous to the environment. In many cases the string of microorganisms used in a specific process has been developed via mutation from a natural string and is able to live only in aseptic conditions. To maintain the aseptecity is often one of the most difficult problems in fermentation technology and is a continuous challenge for process engineers in this field. ROLE OF CONTROL ENGINEERING IN BIOTECHNOLOGY Fermentation is quite a new field for control engineers. However, during the last ten years considerable progress has taken place and even computer control is feasible technology in this field today, see e.g. Nyiri (1972), Jefferis III (1975, 1976), Swartz and others (1976). Generally taken control has an important role in fermentation processes. To maintain acceptable environmental conditions in a fermentor it is mostly necessary to have certain basic control loops such as flow, temperature, pH, level and pressure. In addition to that, production control is often profitable, because fermentations are usually sensitive to different kinds of disturbances, which may easily change production conditions. Such disturbances can originate in environmental conditions, such as lack of certain nutrient or unsuitable pH, or from the organism itself, e.g. from string genetic modification. The disturbances may be long lasting because of long time constants related to the biological growth process. In the worst case the process must be shut down and started again by inoculation. Restarting a plant may take several days. Energy savings may be also considerable. For example in the activated sludge process the aeration
rate can be controlled according to the load variations, which saves 15-20 % electrical energy. The control policies which maintain optimum production conditions in different situations thus often result in much better production profit compared to the uncontrolled case. However, in applications there exist serious difficulties, which are related to instrumentation on one hand and sufficient knowledge of process behaviour on the other hand. Many essential process variables, such as biomass and substrate concentrations, are often impossible to measure with reliable on-line instruments. This means that qUite few such variables which are closely related to the biochemical process can be directly measured. Regular and expensive laboratory analysis are often needed in process monitoring. Process behaviours are in many cases complicated, especially this concerns the cases where secondary metabolities are produced. On the other hand fermentation processes have usually long time constants (5-20 h) and some predictive models are necessary if reasonable process controls are wanted. For this reason process modelling is of considerable interest and is presently an intensively studied field in biotechnology. In many cases, especially when a pure string of microorganism is cUltivated and a growth related product is considered, the advanced models fit amazingly well. However, in other cases validity of the models may be poor and in some cases it has turned out impossible to present a model for the process (e.g. beer making is a good example of this). In the next chapter process analysis and modelling are considered in more detail. ANALYSIS AND MODELLING OF FERMENTATIONS The functioning of a fermentation process is based on enzymatic reactions related to cell metabolism. The number of interactive reactions may be several hundred and in most cases not very precisely known. For practical control purposes there is usually no sense to begin the model building on this basis. On the other hand neither the complete blackbox approach is fruitful, because it does not explain the course of the main biochemical actions in the process. Understanding and monitoring of these actions are often necessary to get reasonable control. The dynamic model may also turn out useful in the design state of the fermentation process, and a blackbox model can not be
A. Halme and A. Holmberg
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used for this purpose. The way commonly chosen is to model the main material and energy balances by considering the cells in the fermentor as a uniform culture, or if required by characterizing the cell mass by suitable distributions e.g. the age distribution. Detailed modelling of mixing circumstances may also be important especially in large scale fermentors. The following actions form usually the basis for the model building: .growth of biomass .assimilation of substrates and nutrients .mass transfer and consumption of oxygen (in airobic processes) • effect of environmental conditions • heat production and transfer In addition to these actions the formation of the product(s) should be usually modelled. This may be an easy or difficult task depending on the product. The products can be classified into two groups, "growth associated" and "nongrowth associated". The products in the first group are normally intracellular, such as protein, and the production rate can be directly related to the growth rate. The second group products may be extracellular or intracellular and usually they are so-called secondary metabolites, such as antibiotics and toxins. Because in this case the product formation is usually not related to any single process variable it is often difficult to obtain a reasonable model. In fact tbe Droduct formation is dependent on the biological states of individual cells. A way to describe this state is the cell age, and the product formation may be related to the age distribution of the culture. There exists quite an extensive literature on the analysis and modelling of fermentations, see e.g. the books by Aiba and others (1973), Atkinson (1974), Pirt (1976), and review articles by Khosrovi and Topiwala (1974) and Yoshida and Taguchi (1976). The biomass growth is usually modelled by using the so-called Monod model (Monod 1950) or its modifications. In the Monod model the growth rate is described by the equation G = I1X,
unit time. The specific growth rate depends on the growth limiting substrate, which usually is the carbon source, by the Michaelis-Menten form I1m S = ~ ,
l1(s)
(2)
s
where s is the concentration of the limiting substrate in the growth medium, 11 is the maximum specific growth ra~e and k is the MichaelisMenten coefficien~. The Monod model can be derived from enzyme kinetics (see e.g. Pirt, 1975). The name originates from the fact that Monod first proved its validity experimentally in microbe cultures. Since then the Monod model has been modified and improved in many ways to consider for example substrate inhibition (Andrews, 1971) and multisubstrate limiting e.g . in the case of carbon and oxygen (Sinclair and Ryder, 1975). Also other type models have been investigated (a survey see e.g. Yoshida and Taguchi, 1977). The effect of environmental factors, such as temperature and pH, may be modelled by considering their effect on the specific growth rate 11 (see e.g. Topiwala and Sinclair, 1971; Andrews, 1971). The effect of temperature seems to be quite simple and is similar to different microbes, although the feasible range of temperature varies. The effect of pH is more complicated and may also have other consequences than changes in the specific growth rate. Both variables usually have optimum values, which may be quite accurate. Utilization of substrates and nutrients is usually described by yield or consumption coefficients. For example it is commonly assumed that a certain amount of substrate consumed produces a certain amount of biomass L'lX
=
( 3)
-Y L'l S,
where Y is the substrate yield coefficient. The substrate consumption in a unit volume is directly related to the growth rate SCON
= Y1 G
•
( 4)
The yield coefficient Y is usually supposed to be a constant, but it may v ary if the environmental conditions vary to a great extent. In the case where the carbon and energy sources are the same substrate its consumption can be divided into three parts
(1 )
(5) where 11 is the so-called specific growth rate and X the biomass density. The equation (1) gives the amount of biomass produced per unit volume per
where L'l S c = substrate needed for cell carbon
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Fermentation Processes
6S EG = substrate needed for growth energy 6S EM= substrate needed for maintenance energy The first two terms are directly related to growth but the third one to the amount of biomass. A more accurate representation for the substrate consumption may thus be obtained from the equation SCON
1 =Y G+mX,
(6)
G
where (7)
and m is the maintenance coefficient. The yield coefficient Y is a consG coefficient tant but the maintenance m may vary e.g. according to the age of cells. The mass transfer of oxygen takes place from the air bubbles to the growth liquid by diffusion process. The microbes are usually able to respirate only dissolved oxygen. The mass transfer to a unit volume of liquid can be described by the equation
(8)
and part of it is released as heat. Also in protein synthesis the chemical energy is not utilized as 100 % efficiency but part of it is released as heat. In practice the heat production is usually linearly related to the oxy gen consumption (see e.g. Imanaka and Aiba, 1976). The total heat balance of a fermentor depends also on other factors, such as the agitation energy, heat input and output via the air flow and other flows, and h e at transfer in heat exhangers. A dynamic model for a continuous fermentation proc e ss, when complete mixing is supported, can be written as follows: Biomass: "' =
( p (S,C)-kD)X-DX,
p (S,C)
=
Pm KS+S'KC+C s c
(10) (11)
Substrate:
s=
(12)
Dissolved oxygen: C = K a(C*-C)-DC-0 2CON L
(13)
Oxygen consumption: 1
02CON = ay p (S,C)X+bX
(14)
Heat:
C*
transfer coefficient present dissolved oxygen concentration (or partial pressur~ saturated dissolved oxygen concentration (or partial pressur~
The transfer coefficient K a depends on the aeration rate and a~itation powers. Additional substances such as antifoam agents may also have an effect on its value. The oxygen consumption can be usually modelled by the equation
( 15) The growth is limited both by substrate and by oxygen. The following notations have been used besides those defined before dilution rate = liquor flow rat~ D emulsion volume influent substrate concentration death coefficient Michael-Menten coefficient fermentor temperature
(9)
which is of the same type as eq. (6). Also in this case the first term descibes the oxygen needed for the growth energy and the second term the oxygen for the maintenance energy. Usually the coefficient a is quite constant but b may vary e.g. according to the cell age. Also other types of oxy gen transfer and consumption models have been suggested. An interesting theory is the direct adsorption mechanism described by Mimura and co-workers (1973) and Halme and co-workers (1977a~. The heat production is related to the energy generation in cell metabolism. Part of the energy obtained in the substrate dissociation process is accumulated as chemical energy (ATP)
cooling fluid temperature _"_"- flow
K
heat transfer area of the coolirB coil (scaled) proportional constant
As is seen from eq . (10)-(15) the fermentation process is quite highly nonlinear. Partly due to this fact and partly because measurement data may be quite uncertain the identification and parameter estimation is often a tedious and time consuming task. Several general identification methods have been tested and special algorithms developed fot fermentation processes. For example extended Kalman filter has been studied by Svrcek and co-workers (1974), a non-
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A. Halme and A. Holmberg
linear least square algorithm by Nihtila and Virkkunen (1977) and a special type of polynomial method by Ribot (1976). An advanced computer system for on-line calculations has been described by Reuss and co-workers (1976). A method found very useful and practical by the authors is the use of sensitivity analysis (D'Ans and co-workers, 1972; Gyllenberg and co-workers, 1975). In the method parameter sensitivity functions are used to help parameter adjusting and to demonstrate the meaning of parameter changes. CONTROL AND OPTIMIZATION Stabilizing Control and Process Monitoring Biological processes are usually open loop stable with respect to the important variables and they work somehow with hardly any control. Thus there exist no principal problems in stabilizing control. However, practical problems related to process monitoring and instrumentation may be considerable. In spite of recent progress in measurement technology, many important variables cannot yet be measured by any automatic means. For example the photo-optical measurement of biomass density, which is the only way used so far, succeeds in quite rare cases. Due to circumstances even such an ordinary measurement as the emulsion volume (level) in a fermentor is often difficult to realise with the accuracy. Sensors like pH, redox, dissolved oxygen should be chosen carefully to guarantee reliable operation. This is especially the case in waste-water treatment processes, where circumstances are hard (see e.g. Aarinen and co-workers, 1978). In aseptic fermentations a special problem may also be due to the fact that sensors have to be changable without losing asepticity. The number of hand measurements is usually high because autoanalyzers are expensive and difficult to use e~ pecially in broths including cells and other solids. On the other hand gas analyzers, such as O - and CO 2 2 analyzers, and gas chromatographs are useful and commonly used devices in many fermentation processes. Besides measuring mass balances, such as oxygen transfer, the information obtained from exhaust gases may be used for indirect measurement or estimation of important process variables e.g. biomass growth rate and concentration (see e.g. Zabriskie and co-workers, 1976; Halme and co-workers, 1977b). A corresponding measurement is also heat production and balance, which can give important indirect in-
formation. The use of estimation techniques provides, however, good process know-how and accurate models. Optimization and Production Control In continuous fermentation processes steady state optimization as usually profitable. In Fig. 2. there is a typical set of curves, in which the biomass concentration, the biomass growth rate and the substrate concentration in a continuous fermentor are shown as functions of the dilution rate the other variables being constant. As is clearly seen the growth rate has the maximum at certain dilution rate. If the product is growth associated (as in this case is) this gives the pOint of maximum production. Economically, however, this need not be the best point to operate, because the consumption of raw materials, e.g. substrate and nutrients, increases with increasing dilution rate. In production fermentations the optimization criterion is usually the net earning, which is to be maximized. If the process is used for waste treatment the "product", e.g. clean water, has usually no~ a price and the criterion may be to attain a gi v en waste removal percentage with minimum operational costs. The best operation point is then usually another than if the same process is used for production. Depending on the process the variables which can be used for optimization are dilution rate, inlet substrate concentration, nutrient feedings, pH, temperature, aeration and agitation powers, and organism or substrate recirculation. A typical continuous industrial fermentation process is protein production, of which an example is given in the next chapter.
Fig. 2. Steady state behaviour of the continuous fermentation process shown in fig. 1. Biomass (X), substrate (S) concentrarions and growth rate (G) as the function of dilution rate (D).
297
Fermentation Processes
In batch fermentations the process is in transient state during the whole operational interval. In optimization the transient is tried to be controlled so that the average net earning is maximized. Such variables as temperature, pH and aeration and agitation powers can be used to control the transient. The batch time is an important optimized variable. It should usually include also the preparation time for the next batch. The preparation time may be relatively long because of cleaning and sterilization operations. A time lag between innoculation and start of growth may be also considerable and should be taken into account. Such difficulties do not appear in continuous operation. An example of industrial processes, which are mostly of batch type are penicillin fermentations. A main reason for batch operation is that the organisms used are sensitive to mutation and may be degenerate after a limited number of doublings. In such circumstances continuous operation is not possible. A way to improve the operation of batch process is a semi-continuous operation. Periodical draw-off or "fed batch" operations (see e.g. Pirt, 1974) are especially suitable for fermentation processes, because the preparation time between batches disappears and even average ~roduct ion may be better than in batch operation. Semi-continuous operation is in fact a type of periodic continuous operation by which optimality of continuous processes may be improved in certain conditions (see e.g. the review by Baile y , 1973). A nice application of this idea in an amino acid fermentation have been presented by Ohno and co-workers (1976). Dynamic optimal control can be used when controling a fermentation process from an operation state to another. Situations in which this may be significant are e.g. start-up procedure, batch control, and change of the operation state during continuous operation. Reasonable criteria are e.g. minimum time, maximal production or minimum cost profit. The problem of controlling a continuous process during transient stages have been studied among others by d'Ans and co-workers (1972), Muzychenko and co-workers (1974) and Takamatsu and co-workers (1974). King and coworkers (1974) have studied the control of a penicillin batch fermentation by using temperature as the control variable. The problems solved have usually lead to bang-bang or singular control policies. In prac-
tice such controls may have the disadvantage that they cause shocks to the microbes, which in turn may disturb the process considerably. However, a feasible suboptimal control ? olicy can often be easily found. CASE EXAMPLE: A PROTEIN PROCESS Single-cell protein (SCP) production for animal or human food is an interesting application of fermentation processes. Several processes have been developed for that purpose, which utilize different substrates and different micro-organisms. An example is the Pekilo-process recently developed in Finland (Forss and co-workers, 1974). At the moment there exis~one production plant in Jamsankoski, Finland. It utilizes sulphite pulping spent liquor and has the capacity of 10.000 tn per year protein, which is used for anima~ food. Quite extensive system eng~n eering and control studies have been done for this process by the authors and co-workers during the last few years and also a computer control system has been desisned for t~e . process. Following short descr~pt~on concerns these studies reported in more detail elsewhere (Halme and coworkers, 1977 a,b). Description of the Pekilo-process In the Pekilo-process SCP for feed purposes is produced from a suitable substrate containing carbohydrates by fermenting a string of paeci~omy ces varioti micro fungi in asept~c conditions. When sulphite pulping spent liquor is used as the su~stra te the process includes the un~t operations shown in the simplified schema in Fig. 1. In the primary treatment S02-content of the liquor is lowered by steam stripping. At the same time liquor is also sterilized, i.e. possible strange organisms are killed. After stripping the liquor is cooled and fed to the fer-
Fig. 3. Microscopic photograph of Paecilomyces varioti microfungl.
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A. Halme and A. Holmberg
mentor, where the microfungi mycelium is grown. A microscopic picture of the mycelium is shown in Fig. 3. Fermentation takes place continuously in one stage without organism or substrate recirculation. Detention time is 3-4 h, pH 4,5-4,7 and temperature 38-39 0 C. The fermentation is aerobic and besides oxygen also nitrogen, phosphorus and potassium are needed as additional nutrients. Oxygen is fed to the fermentor through bubble aerators, nitrogen as ammonia in connection with pH-control. Phosphorus is fed as phosphorus acid (P205) and potassium as potassium chloride (KC1) into the liquid stream in the stripper unit. The process product is mycelium, which is first separated from the fermentor effluent by filtering, then washed (washing takes place in the filter unit), after that dried, first mechanically and then by heat. Finally the product may yet be pressed to pills or some other form. The raw protein content of the product is high, 55-60% w/w, and it can be used as a protein source for animals (in principle also for man) as it is or in feed mixtures. The Jamsankoski production plant includes two parallel strippers and two fermentors, 360 m3 each, and a so-called inoculation line for start up of the process which consists of a 15 m3 inoculation fermentor and a 150 1 laboratory fermentor. !-lore details from the Pekiloprocess can be found in references (Forss and co-workers, 1974; Romantzuk, 1974). Instrumentation and process operation have been described by Halme and co-workers (1977 b) . Process AnalYSis and Computer Control Extensive analysis and modelling studies of the Pekilo fermentation have been done to improve the process design on the one hand and control on the other hand. A dynamic model constructed for control purposes is based on Monod growth model, a direct adsorption for the oxygen transfer and modelling of the cell age distribution. The latter is used to model variations in protein content and oxygen yield coefficient. This makes it possible to estimate the growth rate from the oxygen consumption. The validity of the dynamic model is amazingly good. Details can be found in references (Halme and co-workers, 1977 a,b). The model is utilized in the computer control system developed to improve monitoring and control of the process. A preliminary study of the computer system has been reported previously by Halme and co-workers (1977 b). The
computer system installed now in the Jamsankoski plant does the usual tasks of a process computer system: data acquisition, analysis, storing and process control actions. Higher level controls are related to e.g. such actions as nutrient and antifoam agent addition, aeration rate and agitator speed control, and growth rate control. Special features of the system are e.g. the high number of hand measurements, high data storing requirements and quite a large analYSis program. Process monitoring and control are done via CRT displays and by using a specially designed operator keyboard. The reporting system gives shift, day, week and month reports. The basic system is similar to that described by Meskanen (1976). The hardware consists of a main computer (POP 11/34), a subcomputer (POP 11/04), two disks, two CRT-displays and two typewriters. The system has been installed first for experimentation in the inoculation line of the plant and will be later enlarged to the production line. CONCLUSION The authors' opinion is that the application of control engineering to fermentation processes is an interesting and challenging new field. Pioneering work has already been done and in future the number of applications will probably rapidly increase with increasing importance of these processes. It is comforting for control engineers to know that many fermentation processes behave in an understandable way and can be controlled even by computers. REFERENCES Nyiri, L.K. (1972). Application of Computers in Biochemical Engineering. Advances in Biochemical Engineering, Vol. 2, SpringerVerlag, Berlin. Jefferis Ill, R.P. (1975). Computers in the Fermentation Pilot Plant. Process Biochem., 10, 23. Jefferis Ill, R.P. (Ed.-)-(1976). Workshop Computer Applications in Fermentation Technology. Ve~ lag Chemie, Veinheim. Swartz, J., Wang, H., Cooney, C.L. and Wang, D.I.C. (1976). Computer-aided yeast production. Abstracts of papers of the fifth international fermentation symposium. Berlin. Aiba, S., Humphrey, A.E., Millis, N.F. (1973). Biochemical Engineering, 2nd ed. Academic Press, New York.
Fermentation Processes
Atkinson, B. (1974). Biochemical Reactors. Pion Ltd, London. Pirt, S.J. (1975). Principles of Microbe and Cell Cultivation. Blackwell, Oxford. Khosrovi, B. and Topiwala, H.H. (1974) Mathematical Modelling and Computer Control in Fermentation Processes. Annual Report on the Progress of Applied Chemistry, 59, 357. Yoshida, T. and Taguchi, H. (1976). Use of Models in Fermentation Control. In R.P. Jefferis III (Ed.) Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. Monod, J. (1950). La technique des cultures continues; theorie et applications. Ann. Inst. Pasteur, 79, 390. Andrews, J.F. (1971). Kinetic models of biological waste treatment processes. Biotechnol. Bioeng. Symp. 2, 5. Topiwala, H. and Sinclair, C.G. (1971) Temperature Relationships in Continuous Culture. Biotechnol. Bioeng. 13, 795. Mimura, A., Takeda, I., Wasaka, R. (1973). Some Characteristic Phenomena of Oxygen Transfer in Hydrocarbon Fermentation. Biotechnol. Bioeng. Symp. 4. Halme, A., Holmberg, A. and Tiussa, E. (1977a). Modelling and Control of a Protein Fermentation Process Utilizing the Spent Sulphite Liquor. Proc. IFAC Symposium on Environmental Systems Planning, Design and Control, Pergamon, Oxford. Imanaka, T. and Aiba, S. (1976). A Convenient Method to Estimate the Rate of Heat Evolution in Fermentation. J. Appl. Chem. Biotechnol. ~, 557. Svrcek, W.Y., Elliott, R.F. and Zajic, J.E. (1974). The Extended Kalman Filter Applied to a Continuous Culture Model. Biotechnolog. Bioeng.16, 827. Nihtila, M. and Virkkunen, J. (1977). Modelling, identification and program development for a pilotscale fermentor. 5th IFAC/IFIP Internarional Conference on Digital Computer Applications to Process Control, North-Holland, Amsterdam. Ribot, D. (1976). Identification of Fermentations by Polynomial Methods. In R.P. Jefferis III (Ed.), Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. D'Ans, G., Gottlieb, D. and Kokotovic, P. (1972). Optimal control of bacterial growth. Automatica, 8, 729. Gyllenberg, A., Hamalainen, R.P~ and Halme, A. (1975). Modelling of Microbial Systems for Process Op-
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timization and Control. Report B 26, Systems Theory Laboratory, Helsinki University of Technology. Aarinen, R., Tirkkonen, J., Halme, A. (1978). Experiences on Instrumentation and Control of Activated Sludge Plants - a Microprocessor Application. 7th IFAC Triennal World Congress, Helsinki. Zabriskie, P.W., Armiger, W.B. and Humphrey, A.E. (1976). Application of Computers to the Indirect measurement of Biomass Concentration and Growth Rate by Component Balancing. In R.P. Jefferis III (Ed.), Workshop Computer Applications in Fermentation Technology. Verlag Chemie, Veinheim. ~euss, M., Jefferis Ill, R.P. and Lehman, J. (1976). Application of an On-line System of Coupled Computers to Fermentation Modelling. Ibib. Halme, A., Kiviranta, H., Kiviranta, M. (1977b). Study of a SingleCell Protein Fermentation Process for Computer Control. Proc. IFAC/ IFIP 5th International Conference on Digital Computer Applications to Process Control, North Holland Amsterdam. Pirt, J. (1974). The theory of fed batch culture with reference to the penicillin fermentation. ~. Appl. Chem. Biotechnol. 24, 415. Bailey, J.E. (1973). Periodic Operation of Chemical Reactors: A Review. Chem. Eng. Commun, 1, Ill. Ohno, H., Nakanishi, E. and Takamatsu, T. (1976). Optimal Control of a Semibatch Fermentation. Biotechnolo Bioeng., 18, 847. Murechenko, L.A., Mascheva, L.A. and Yakovleva, G.V. (1974). Algorithm for optimal control of microbiological synthesis. Biotechnol. Bioeng. Symp. 4. Advances in Microbial Engineering, ~, 629. Takamatsu, T., Hashimoto, I., Shionya, S., Mizuhara, K., Koihe, T. and Ohno, H. (1975). Theory and practice of optimal control in continuous fermentation process. Automatica, 11, 141. King, R.E., Aragona, J. and Constantinides, A. (1974). Specific optimal control of a batch fermentor. Int. J. Control, 20,869. Forss, K., Passinen, K. an~Sjostrom, E. (1974). Proc. Symp. Wood Chemistry, Pure and Applied, ACS meeting, Los Angeles. Romantchuk, H. (1974). The Pekiloprocess. Single Cell Protein, MIT Press. Meskanen, A. (1976). Design of the Man-Machine Interface for Computer Coupled Fermentation. In R.P. Jefferis III (Ed.) Workshop Computer Applications in Fermentation Technology, Verlag Chemie.