Integration of process control systems, simulation tools and on-line analytical techniques in complex bioreaction processes

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Brendan Curtis
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1 R. Luttmann Research Center of Bioprocess Engineering and Analytical Techniques University of Applied Sciences Fachhochschule Hamburg Lohbrügger Kirchstr. 65, D Hamburg, Germany 1 Integration of process control systems, simulation tools and on-line analytical techniques in complex bioreaction processes During the last years one amplified research topic of the Laboratory of Bioprocess Automation at the University of Applied Sciences in Hamburg was the development of a reliable bioreactor process control system including methods and concepts of modern biochemical engineering such as: - aplication of industrial process control in bioreaction processes - decentralized measurement and control equipment linked via CAN-(Controller Area Network-) field bus - integration of real time simulation tools into the process control software - mathematical modelling of complete processes under investigations in order to allow control development with a virtual bioreaction system (apparent processing /1/) - linkage of PC s to the control system via Ethernet. (WKHUQHW 3& 0$7/$% 8%,&21 3URFHVV&RQWURO &$1)LHOGEXV,QVWUXPHQWDWLRQ 1LWULILFDWLRQ &$1 )ORZ $QDO\VLV 2II*DV $QDO\VLV,QVWUXPHQWDWLRQ 'HQLWULILFDWLRQ &$1 ) % ) ) 1 ) ) & ) + ) % 1LWULILFDWLRQ 'HQLWULILFDWLRQ 1LWURJHQ :DVWH:DWHU )HHG +DUYHVW %DODQFLQJ &$1 0HWKDQRO :DVWH:DWHU Figure 1: Automation structure of a plant for simultaneous pollutant degradation

2 2 As an example fig. 1 shows the automation structure of a plant for simultaneous degradation of nitrogen and methanol loaded waste waters. Both reactor systems are hydraulically connected but microbiological disconnected by cross flow membrane systems. The decentral components of measuring, controlling and manipulating the process are linked to each other through the CAN-field bus with the process control system UBICON. These components include the bioreactor equipment, off gas analysis systems to measure O2, CO2, NO, NO2 and NOX, flow injection analysis (FIA) for photometric on-line determination of ammonia and nitrite and flow diffusion analysis (FDA) for on-line detection of methanol via biosensors. Besides a powerful formula interpreter the process control system UBICON offers especially the opportunity to evolve the process by using the principle of apparent processing /1/. The system provides the user a complete mathematical model of the plant to test his control concepts with real time simulations /2/. Figure 2: Two stage bioreaction system with modern process control equipment

3 3 To run off-line simulations e.g. to identify process parameters during the active process for process prediction UBICON is connected to a personal computer via ethernet and DDE. Powerful simulation tools e.g. like in MATLAB can be used here. More over it is recently possible to watch and (partially) control processes running in the Bioprocess Automation Lab of the University of Applied Sciences via internet /3/. Fig. 2 shows a photography of the waste water treatment plant, built up with industrial bioreactors from B. Braun Biotech International in Melsungen, process control from esd electronic system design in Hannover and on-line analysis systems from TRACE Biotech AG in Braunschweig. Similar high instrumented reactors in a scale from 1 l to 15 l were assembled in the lab for production of pharmaceutical products. An example in industrial biotechnology will be discussed with an integrated processing for production of the antagonist of Monocyte Chemoattractant Protein 1 (MCP-1) with Pichia pastoris.,, po 2, µ adaption phase po 2 repression phase high cell density cultivation production phase µ 1max c XL µ µ 1w po 2w µ 2max t Figure 3: Process course for production of recombinant proteins with Pichia pastoris

4 4 Fig. 3 principally shows a time course of the cultivation. Plotted are the glycerol concentration c S1L, the methanol concentration c S2L, the cell mass concentration c XL, the product concentration c PL, the dissolved oxygen tension po 2 and the specific growth rate µ. The process is devided into four phases: Phase 1: Adaption phase (batch with glycerol) Phase 2: Repression phase (batch with glycerol and po 2 /agitation-control) Phase 3: High cell density cultivation (fed batch with µ-stat procedure /4/: po 2 -control by glycerol feed and µ-control by agitation) Phase 4: Production phase (fed batch with methanol/feed-control and po 2 /agitation-control) This process is running completely automatically where the process control procedure is developed by apparent processing. Fig. 4 gives an image of the appearing problems and some control tasks. agitation control M po 2w c S2L FDA methanol control N St po 2 po 2 control N Stw F R2 F c M F H S2Lw L V L F Hw c S2R2 V Lw weight control methanol feed harvest Figure 4: Control concept during the production phase of recombinat proteins

5 5 Beside classical po 2 -control by agitation, the problems of methanol concentration-control and perfusion engineering have to be solved. Methanol is measured on-line by a Flow Diffusion Analysis system of TRACE Biotech with a low measuring cycle (3-30 min). The control is carried out using model based procedures, where the current substrate uptake rate and the following feed rate are calculated on-line from FDA-measurement. Fig. 5 shows the general process just under progress. M micro filtration integrated down stream processing off-line down stream processing L F M F H weight control ultra filtration F LC LC F HPLC HPLC cultivation and production heating/cooling harvest waste F U product Figure 5: Integrated bioprocessing for production and purification of heterologous proteins Primary down stream steps like microfiltration and ultrafiltration are integrated into the cultivation procedure. A lot of open loop control tasks are implemented for membrane recovery and reduction of membrane fouling. This announced contribution should give an insight in the above described opportunities to automate processes in bioreactors.

6 6 References /1/ Luttmann, R. and Gollmer, K.: Control development of bioreaction process with on-line simulation methods. In: K. Schügerl, K.-H. Bellgardt (ed): Bioreaction Engineering Vol. 4, Modelling and Control. To be published in 2000, Springer Heidelberg, New York, Hongkong, London, Paris, Tokyo. /2/ Luttmann, R., Bitzer, G., Müller, D., Scheffler, U. and Friedriszik, U. (1995): Development of a µ-stat with Online-Simulation Methods. PERGAMON A Postprint volume from the 6 th International Conference Computer Applications in Biotechnology, Garmisch Partenkirchen, Germany, May 1995: /3/ http// /4/ Luttmann, R., Bitzer, G., Hartkopf, J. (1994): Development of control strategies for high cell density cultivations. Mathematics and Computers in Simulation 37:

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