Sliding-mode control of fermentation processes

Златева, Пламена

doi:10.1007/s004490050340

Cited by 18 publications

(23 citation statements)

References 10 publications

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“…The uncertainty in the model satisfies inequality (9). The simulation model is parameterized as in Bastin and Dochain (1991) and Zlateva (1997) so that m max ¼ 0:4, k s ¼ 0:4, k ¼ 27:3, S in ¼ 3 and the ranges for the intervals of the biomass concentration, substrate concentration and dilution rate are: 0.01 X 0.2, 0.1 S 1.8, 0 D 0.9.…”

Section: Performance Of the Observermentioning

confidence: 99%

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Estimation and control of non-linear variables in a continuous fermentation process using sliding mode techniques

Rahman

Spurgeon

Yan

2011

Transactions of the Institute of Measurement and Control

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Biomass, substrate or metabolite concentrations are difficult to measure online in fermentation processes because of the lack of reliable, cheap and sterilizable transducers. Currently, many of the measurements required may be determined through offline analysis, which is costly and time consuming. Furthermore, the specific growth rate conditions involved in the fermentation are typically non-linear and uncertain. In this paper, a new variable, the substrate consumption rate, consisting of a combination of substrate concentration, biomass concentration, specific growth rate and yield production coefficient, is introduced to overcome these problems and simplify the non-linear differential equations of the fermentation process. A sliding mode observer, which only requires measurement of substrate concentration, is then developed to solve the estimation problem, providing a soft sensor to estimate the substrate consumption rate. It is shown that the sliding mode exhibited by the corresponding observer error dynamics is exponentially stable. This parameterization and the resulting estimate of biomass concentration are then utilized within a feedback control strategy. Non-linear simulation results in the presence of both parameter uncertainties and external disturbances illustrate the approach.

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Section: Performance Of the Observermentioning

confidence: 99%

“…A continuous fermentation process is considered to illustrate the methodology and the system is described by the following second-order non-linear system (Zlateva, 1997);…”

Section: Fermentation Modelmentioning

confidence: 99%

Estimation and control of non-linear variables in a continuous fermentation process using sliding mode techniques

Rahman

Spurgeon

Yan

2011

Transactions of the Institute of Measurement and Control

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“…In this work, a continuous fermentation process is considered and the system is described by the following second order nonlinear system (Zlateva, 1997);…”

Section: Fermentation Processmentioning

confidence: 99%

“…The dynamic characteristics of these microorganisms are highly nonlinear and the model parameters do not remain constant for long periods due to metabolic variations and physiological modifications (Bastin and Dochain, 1991, Tzonkov et al, 1992, Tokat, 2009. Moreover, the complexity and uncertainty of the dynamics of fermentation limits the ability of conventional controllers to provide desired performance even if the process conditions are constant during fermentation (Zlateva, 1997, Dochain, 2003, Tham et al, 2003. A number of modern control approaches have been applied to these processes but robustness problems and issues with complexity of the design remain (Bastin and Dochain, 1991).…”

Section: Introductionmentioning

confidence: 98%

Sliding Mode Observer based Control for a Continuous Fermentation Process

Rahman

Spurgeon

Yan

2010

UKACC International Conference on CONTROL 2010

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This paper will develop an observer based control using sliding mode techniques to obviate the need to measure biomass concentration. A new variable, the substrate consumption rate, consisting of a combination of substrate concentration, biomass concentration, specific growth rate and yield production coefficient is introduced to simplify the nonlinear differential equations of the fermentation process. A sliding mode observer is then developed to estimate this substrate consumption using readily available measurements of substrate concentration. It is shown that the sliding mode exhibited by the corresponding observer error dynamics is exponentially stable. This parameterisation and the resulting estimate of biomass concentration is then utilised within a feedback control strategy. Nonlinear simulation results in the presence of both parameter uncertainties and external disturbances illustrate the application of the approach.

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“…Different reactor configurations were examined in terms of their controllability properties [8]. Sliding mode control has been used in continuous fermentation processes to keep the culture in steady state [9][10][11]. An optimal controller to operate a continuous culture around maximum productivity was reported by Alvarez and Alvarez [12].…”

Section: Introductionmentioning

confidence: 99%