Online adaptive control of a fermentation process

Williams, David L.; Yousefpour, P.; Swanick, B.H.

doi:10.1049/ip-d.1984.0021

Cited by 13 publications

(9 citation statements)

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“…Furthermore, to optimise both cell numbers and product yield from substrate feed requires dual optimisation of two interacting control loops. Previous work [7] demonstrated that, even with an adaptive controller operating on a single variable, the two requirements cannot be optimised. Only by employing a multivariable controller with changing parameters can both objectives be achieved.…”

Section: Resultsmentioning

confidence: 98%

“…The process states may be reconstructed from the estimated parameters as shown by Jazwinski [15]. In the adaptive scheme used by the authors, the nonlinear timevariant process states are represented in terms of the linear process transition and control driving matrices 4>{T) and A(T) [7], which are updated at each sample interval. These results, together with the online measured variables temperature, flow rate, pH, DOT, RQ and stirrer speed, are logged over the duration of the fermentation.…”

Section: Reconstruction Of Process Variablesmentioning

confidence: 99%

“…Hence, it is either necessary to estimate the cell mass online using an observer, such as a Kalman filter, and applying a self-tuning technique [13,14], or alternatively controlling the environment to maximise cell growth [7].…”

Section: Control Objectivementioning

confidence: 99%

“…The adaptive control technique utilised was first developed in 1970 [4,5] and has been successfully applied to several processes [6,7]. This adaptive technique was selected because the process is described in terms of first-order differential equations which are directly related to process measurements.…”

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confidence: 99%

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Multivariable adaptive control of baker's yeast fermentation

Williams

Montgomery

1986

IEE Proc. D Control Theory Appl. UK

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The paper describes an ongoing investigation into the application of a multivariable adaptive control technique to a fed-batch baker's yeast fermentation. The application of conventional 3-term controllers provides poor control due to problems in tuning individual loops, owing to the time-variant and nonlinear properties of the process. Furthermore, the main process variable, cell mass, cannot be measured directly and hence control must be effected by either estimating the cell mass online or, alternatively, by controlling the environment for optimal cell growth. The latter approach requires the simultaneous optimisation of two or more process variables, requiring multivariable techniques. Results are presented describing the practical problems of establishing a suitable structure for the adaptive technique and the experience gained in using recursive least-squares estimation of process variables. The paper also includes results of the online application of multivariable adaptive control on a pilot-plant fermenter.

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Section: Resultsmentioning

confidence: 98%

Section: Reconstruction Of Process Variablesmentioning

confidence: 99%

Section: Control Objectivementioning

confidence: 99%

mentioning

confidence: 99%

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Multivariable adaptive control of baker's yeast fermentation

Williams

Montgomery

1986

IEE Proc. D Control Theory Appl. UK

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“…The convergence time for parameters in the adaptive control can be reduced by using a priori knowledge of the initial parameters (Williams and Montgomery, 1986). Reported applications of adaptive control schemes include controlling pH and temperature of the fermenter (Melin et al, 1982), waste water treatment (Bastin et al, 1982), baker's yeast (Williams et al, 1984;Williams and Montgomery, 1986;Ramseier et al, 1992), penicillin (Montague et al, 1985), alcohol (Samaan et ~rl, 1990;Roux et al, 1992) and so on. In these approaches, the key process variables are usually required to be measurable on-line.…”

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confidence: 98%

Modelling and control of fed-batch fermentation processes: towards improved quality and productivity

Zhang

Vanichsriratana

Tang

et al. 1997

Transactions of the Institute of Measurement and Control

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This paper describes research into modelling, estimation and control of fed batch fermentation and an application to a large fermentation plant producing secondary metabolites. The problems are examined in a general sense from the viewpoints of both biological science and control engineering. Very extensive modelling and estimation work has been carried out. Only the most significant outcomes are reported here but references to the detailed research are given. Some of the research has resulted in the development of a novel advanced supervisory system -the Intelligent Process Management System (IPMS). Experience of applying the IPMS to a large-scale production process is described. The software has been designed on a modular basis and although implemented initially at one production plant, the approach is generic and fundamentally plantindependent so that further application to a range of other plants is envisaged.

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Dynamic optimization of hybridoma growth in a fed-batch bioreactor

Dhir

Morrow

Rhinehart

et al. 2000

Biotechnol. Bioeng.

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This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process-model mismatch at each sampling time, updated the model parameters, and re-optimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single offline optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization online increased yield of cell mass per batch by 44% over the single offline optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture.

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Online adaptive control of a fermentation process

Cited by 13 publications

References 0 publications

Multivariable adaptive control of baker's yeast fermentation

Multivariable adaptive control of baker's yeast fermentation

Modelling and control of fed-batch fermentation processes: towards improved quality and productivity

Dynamic optimization of hybridoma growth in a fed-batch bioreactor

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