Comparative evaluation of various control schemes for fed-batch fermentation

-, Hisbullah and Hussain, Mohd Azlan and Ramachandran, K.B. (2002) Comparative evaluation of various control schemes for fed-batch fermentation. Bioprocess and Biosystems Engineering, 24 (5). pp. 309-318. ISSN 1615-7591, DOI

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The crucial problem associated with control of fed-batch fermentation process is its time-varying characteristics. A successful controller should be able to deal with this feature in addition to the inherent nonlinear characteristics of the process. In this work, various schemes for controlling the glucose feed rate of fed-batch baker's yeast fermentation were evaluated. The controllers evaluated are fixed-gain proportional-integral (PI), scheduled-gain PI, adaptive neural network and hybrid neural network PI. The difference between the specific carbon dioxide evolution rate and oxygen uptake rate (QcQo) was used as the controller variable. The evaluation was carried out by observing the performance of the controllers in dealing with setpoint tracking and disturbance rejection. The results confirm the unsatisfactory performance of the conventional controller where significant oscillation and offsets exist. Among the controllers considered, the hybrid neural network PI controller shows good performance.

Item Type: Article
Additional Information: Cited By (since 1996): 13 Export Date: 5 March 2013 Source: Scopus CODEN: BBEIB Language of Original Document: English Correspondence Address: Ramachandran, K.B.; Department of Chemical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; email: References: Aiba, S., Nagai, S., Nishizawa, Y., Fed-batch culture of Saccharomyces cerevisiae: A perspective of computer control to enhance the productivity in baker's yeast cultivation (1976) Biotechnol Bioeng, 18, pp. 1001-1016; Wang, H.Y., Cooney, C.L., Wang, D.I.C., Computer control of baker's yeast production (1979) Biotechnol Bioeng, 21, pp. 975-995; Johnson, A., The control of fed-batch fermentation processes: A survey (1987) Automatica, 23, pp. 691-705; O'Connor, G.M., Riera, F.S., Cooney, C.L., Design and evaluation of control strategies for high-cell-density fermentations (1992) Biotechnol Bioeng, 39, pp. 293-304; Boskovic, J.D., Narendra, K.S., Comparison of linear, nonlinear and neural network- based adaptive controllers for a class of fed-batch fermentation processes (1995) Automatica, 31, pp. 817-840; Chen, L., Bastin, G., Van Breusegem, B., A case study of adaptive nonlinear regulation of fed-batch biological reactors (1995) Automatica, 1, pp. 55-65; Dairaku, K., Izumoto, E., Morikawa, H., Shioya, S., Takamatsu, T., An advanced micro-computer coupled control system in a baker's yeast fed-batch culture using a tubing method (1983) J Ferment Technol, 61, pp. 189-196; Takamatsu, T., Shioya, S., Okada, Y., Profile control scheme in a baker's yeast fed-batch culture (1985) Biotechnol Bioeng, 27, pp. 1675-1686; Park, Y.S., Shi, Z.P., Shiba, S., Chantal, C., Iijima, S., Kobayashi, T., Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture (1993) Appl Microbiol Biotechnol, 38, pp. 649-655; Lee, J., Lee, S.Y., Park, S., Middelberg, A.P.J., Control of fed-batch fermentations (1999) Biotechnol Adv, 17, pp. 29-48; Rani, K.Y., Rao, V.S.R., Control of fermenters: A review (1999) Bioprocess Eng, 21, pp. 77-88; Schubert, J., Simutis, R., Dors, M., Havlik, I., Lubbert, A., Bioprocess optimization and control: Application of hybrid modeling (1994) J Biotechnol, 35, pp. 51-68; Dayal, B.S., Taylor, P.A., Macgregor, J.F., The design of experiments, training and implementation of nonlinear controllers based on neural networks (1994) Can J Chem Eng, 72, pp. 1066-1079; Sonnleitner, B., Kappeli, O., Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis (1986) Biotechnol Bioeng, 28, pp. 927-937; Shimizu, K., Morikawa, M., Mizutani, S., Iijima, S., Matsubara, M., Kobayashi, T., Comparison of control techniques for baker's yeast culture using an automatic glucose analyzer (1988) J Chem Eng (Jap), 21, pp. 113-117; Montague, G., (1997) Monitoring and control of bioreactors, , Institution of Chemical Engineers, UK; Albiol, J., Campmajo, C., Casas, C., Poch, M., Biomass estimation in plant cell cultures: A neural network approach (1995) Biotechnol Prog, 11, pp. 88-92; Pertev, C., Turker, M., Berber, R., Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters (1997) Comput Chem Eng, 21, pp. S739-S744; Nilsson, A., Taherzadeh, M.J., Liden, G., Use of dynamic step response for control of fed-batch conversion of lignocellulosic hydrolyzates to ethanol (2001) J Biotechnol, 89, pp. 41-53; Tsoukalas, L.H., Uhrig, R.E., (1997) Fuzzy and neural approaches in engineering, , Wiley, Chichester; Aziz, N., Hussain, M.A., Mujtaba, I.M., Optimal control of batch reactors using generic model control (GMC) and neural network (2000) Proceedings of The 10th European Symposium on Computer-Aided Process Engineering, 2000, p. 175. , Pierucci S (ed), Elsevier Science, Amsterdam; Aziz, N., Hussain, M.A., Mujtaba, I.M., Performance of different types of controllers in tracking optimal temperature profiles in batch reactors (2000) Comput Chem Eng, 24, p. 1069
Uncontrolled Keywords: carbon dioxide; glucose; oxygen; article; comparative study; controlled study; evaluation; fed batch fermentation; mathematical analysis; nerve cell network; nonhuman; oscillation; oxygen consumption; priority journal; Saccharomyces cerevisiae; simulation; yeast; Saccharomyces.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 07:05
Last Modified: 10 Feb 2021 03:41

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