Strategies for Optimal Dissolved Oxygen (DO) Control

Copyright © IFAC Computer Applications in Biotechnology. Garmisch-Panenkirchen. Germany. 1995

STRATEGIES FOR OPTIMAL DISSOLVED OXYGEN (DO) CONTROL R. MikJer, W. Kramer, O. Doblhoff - Dier, K. Bayer

institute for Applied Microbiology. University ofAgriculture Nufidorfer Lande 11. A-1190 Wien

Abstract: The exponential growth ofbiomass and the frequent changes of environmental conditions (fluid characteristics, addition of antifoam, etc.) create problems in DO control offennentations in lab and pilot scale . Traditionally DO is controlled by application of conventioanl control algorithms. as PID, connected to a control cascade. The perfonnance of the control loop depends on the oxygen consumption. For DO loop optimization of E. coli fennentation empirical adaption of tuning parameters and fuzzy tuning was applied . Keywords: cascade control, fuzzy control, loop tuning

1. INTRODUCTION Fig. 1

Batch culture of single cell microorganisms can typically be divided into growth phases referred to as lag phase, exponential phase, declination (late log) phase, stationary phase and death phase. A number of aberrations, such as the diauxic growth of organisms (sequential utilisation of two different substrates) can be observed, but in principle biomass will increase during batch culture in a more or less exponential way and growth will slow dO\m and stop on substrate limitation. The control of dissolved oxygen in microbial fennentations creates problems on both laboratory and pilot/production scale. This is most frequently due to: @ The increasing over all oxygen demand with increasing biomass @ Changing mass transfer coefficient due to changes of fluid characteristics by metabolic products @ Foaming at high aeration rates and high stirrer speed • Changing mass transfer coefficient due to addition of anti-foam agents The control of oxygen is even more complicated if it is controlled by both airflow or oxygen concentration in the aeration gas and stirrer speed. In this case traditional control strategies would normally employ proportional integral differential (PID) algorithms connected to a control cascade (fig. 1).

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Fig. 1: Convenuonal control strategies (cascaded) applied to the control of dissolved o:'>,:ygen As the control loop IS dependent on the overall oxygen consumpuon. which increases dramatically with exponentially proliferating biornass. optimal tuning of the controller will be linked to o>.:ygen uptake rate .

2. MATERIALS AND METHODS 2.1. ]-.fodel fermentatIOn ' To test the strategies for dissolved o"'1'gen PID loop tuning a model batch fennentation was perfonned using E. coli HMS174 , which is used for the expression of recombinant proteins (!(ramer et al, 1994). Standard cultures were gro\m on agar slants and stored at 4°(, Inoculum was prepared in shaking flasks in 30 rnl fennentation medium (see table 1), inoculum was then transferred to the fennenter. Fennentation was carried out at 37,0 QC and pH 7.0.

Fermentation medium was adjusted for 4g/l bacterial dry weight (BDW) for inoculum and for 10 gIl BDW for fermentation. Table 1: Fermentation medium composition Glucose (autoclave separate) KH 2P0 4 K 2HP04 ~hS04 ~CI

Dissolve separate: Na)Citrate MgS0 4.7H20 CaCh.2H 20 Trace element solution see table 2 (adjust to pH 7.0)

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2.3. Software strategies for process dependent tuning of DO controller At the Institute for Applied Microbiology (lAM) two different strategies to tune the DO control loop in dependance of fermentation progress (biomass and oxygen uptake increase) were developed.

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singleton type only, therefore the defuzzification algorithm. based on the centre of gravity method. is much simplified by using the maximum height to the left and right. The industrial programmable controller is linked to a IBM compatible personal computer. Data logging and trendig, using in-house developed software and programming tasks, using the OMRON graphical Fuzzy Support Software (FSS). were run on the personal computer. Amplified signals are converted to digital by the SYSMAC C200H and processed by in-house developed software.

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Fermentation equipment consisted of a stirred tank (71) 51 working volume fermenter (MER Switzerland) \-vith a double 6 flat blade type SUrrer (ratio stirrer diameter: fermenter diameter=l:2). bottom magneuc coupled drive (150 - 1300 RPM) and ring sparger. The stand-alone control unit (lMCS 2000. MBR S"ltzeriand) is equipped "ith signal amplifiers and dedicated PID controllers for temperature. pH. dissolved oxygen. air flow and rpm. An flow was controlled by a flow controller consisting of a thermal flowsensor. PID controller and continuous magnetic control valve . Medium is sterilized in-situ during fermenter sterilisation

Empirical PID tuning In a typical fed batch fermentation (10 I working volume) the oxygen transfer rate is about 0,5g 01/h at the beginning and increases to about 30-40g O~/h at high density of biomass at the end of fed batch fermentation Due to this wide range of OTR the parameters of the PID controller (hp, T v. T N) have to be adapted to actual conditions to attalO optimum control of DO during the whole fermentation . PID parameter adaption was achieved by optimization at an average oX'ygen transfer rate of about 5-10 g O:/h according to Ziegler and Nichols (1942). On the basis of these opumized parameters adaption of Kp valid for other ranges of OTR was achieved by multiplication of optimum Kp with the ratio of ,.flowact to flow opt" (flowopt represents the flow at which optimum parameters were evaluated) A scheme of the opurruzed controller is shown in figure 2: Fig. :2

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System hard- and software For the advanced control of dissolved o>.:ygen, the stand-alone control unit was connected to an industrial programmable controller (SYSMAC C200H, Ornron.Japan). The SYSMAC C200H can be eqUipped with a fuzzy control co-processor (FP3000, Omron. Japan) via an integration unit (C200H-FZ001 , Ornron. Japan) . This co-processor can handle a maximum of 7 linguistic labels with S, Z, triangular and 4 point trapeze membership functions, 3 control groups with a maximum of 128 rules per group, up to 8 (if...) conditions and up to 2 conclusions (then ... ) per rule. The membership functions of defuzzification are of

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Fig. 2 : DO control loop applying optimized PID parameters In order to avoid instability of the controller the actual flow (flow.. ) is averaged during a period of one to several minutes. This strategy has been used successfully ID many cases.

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As outlined, the PID algorithm adjusts the output of the controller in dependance of the process value's deviation from the setpoint (proportional) or error e, the process value's rate of change (differential) and takes into account the past development (integral).

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Fuuy PID tuning The process engineer's expert knowledge, based on loop tuning eXl'eriments. during different stages of fermentation, using the tuning rules developed by Ziegler and Nichols (1942) was used for a new approach to dissolved oxygen control with fuzzy control algorithms. To test the system. oxygen consumption by microorganisms was simulated by sparging nitrogen into the fermenter This set-up has the advantage of the possibility to keep the simulated ox)'gen consumption at a constant level or change it dynamically (simulate exponential grov.th) . The second approach to achieve more stable control of dissolved oxygen. was based on the fuzzy tuning of a conventional PID loop. Expert knowledge was used to adjust the PID tuning parameters in dependance to the oxygen consumption, estimated by the average air flow. Figure 3 shows the structure of the control loop Fig. 3 \I.

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Fig. 7: Evaluation of Tv (sec) As experimental results showed_ o:',ygen input at the end of the exponential phase was limited by insufficient rpm. On the other hand higher stirrer speeds are problematic at the beginning of the fermentatIOn. due to too high oxygen input, even at zero aeration. excessive foaming and unnecessary power input. Therefore oxygen input was additionally controlled by increasing rpm in relauon to average flow (rpm = % average flow • 1. 64). 1% of flow corresponds to 0 .05 I air lmin or 0 .01 vvm at a fermentation volume of 5 1.

3. RESULTS AND DISCUSSION

In the following figures (4-7) the membership functions of the input and output parameters are shown:

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3. J.Empirical PlD tunmg Although the applied tuning algorithm is very simple. it provides excellent stabilisation of the control loop at various stages of the batch. It is important to note, that the loop tuning is always valid for only the one type of organism, defined media and culture

conditions the original optimisation was performed for. In many cases this strategy has been used with success. At the lAM the control of dissolved oxygen concentration in fermentation of recombinant E. coli could be optimized by adjusting the PlO loop nme parameters in dependance of the actual aeration rate. The application of this control strategy proved tight control of DO in E. coli fermentations up to 60 g BDWIl.

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Fig. 10: K p , T N, T v and rpm set point With this approach acceptable dissolved o""ygen control could be achieved. A number of different strategies, such as the fuzzy control of dissolved oX'ygen without the use of conventional PID algontms has been tested and will be published elsewhere . The big advantage of the PID fuzzy tuning is the relative simplicity of rules and membership functions. Especially for industnal fermentations this approach may be used for a relatively safe performance enhancement. as it can be performed by a ex1ernaJ computer and tuned parameters can be downloaded to the mdustrial controller. Parameters can be double checked to stay within oparational limits to mcrease operauonaJ safety.

REFERENCES Kramer, W" G Elmecker, R. Weik, D . Mattanovich and K. Bayer (1994) . Kinetic studies for the optimiZJltion of recombinant protein formation. Annals of the New York Academy of Sciences, in press Ziegler. J.G. and N.B. Nichols (1942). Optimum Settings for Automatic Controllers. Trans. ASAfE 64, 759

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