Modelling and estimation aspects of adaptive predictive control in a fermentation process

Roux, G.; Dahhou, B.; Queinnec, Isabelle

doi:10.1016/0967-0661(95)00206-1

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Many applications are described in the literature for GPC and its variants. In , an application to batch‐fed fermentation for penicillin production is presented, whereas in , modeling and estimation aspects in fermentation processes are examined. In , a multivariable GPC algorithm with feedforward is applied to anesthesia.…”

Section: Optimized Adaptive Control From the Optimization Perspectivementioning

confidence: 99%

Survey of industrial optimized adaptive control

Martín‐Sánchez

Lemos

Rodellar³

2012

Adaptive Control & Signal

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This survey paper aims, to present in a systematic way, the 'state-of-the-art' of a class of so called optimized adaptive control methodologies, where adaptive systems theory is complemented by optimal control theory. It is based on literature published since 1958 and emphasizes results with proven industrial relevance. After recalling previous adaptive systems, results that had prepared the ground for the new ideas, the analysis departs from the work on adaptive predictive control in the mid 1970s, which provided the conceptual basis for the development of this kind of technique. Well-known design methods, both from the stability and optimization perspectives, along with their results, are interpreted in an intuitive way in order to understand how control law and the adaptive mechanism complement each other to produce mature controllers that can reliably solve the industrial process dynamic stabilization problem. Surveys of industrial applications from both design perspectives are presented and industrial products based on optimized adaptive control are cited.

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Section: Optimized Adaptive Control From the Optimization Perspectivementioning

confidence: 99%

Survey of industrial optimized adaptive control

Martín‐Sánchez

Lemos

Rodellar³

2012

Adaptive Control & Signal

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“…Estimators based on the RLS method consider a linear model for the system within the time interval, where the identification procedure is performed. Some authors have shown the capacity of linear estimators to adequately estimate the fermentation process without increasing the model structure complexity 42, 43. In this way, some approaches have been developed to carry out the estimation from measurement of oxygen31 or carbon dioxide in the exhaust gas of the fermentor 32.…”

Section: Estimation Algorithmsmentioning

confidence: 99%

State and specific growth estimation in heterologous protein production by Pichia pastoris

Barrigón¹,

Ramon²,

Rocha³

et al. 2011

AIChE Journal

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Estimation of biomass, substrate, and specific growth rate (μ) by two nonlinear observers (nonlinear observer‐based estimator—NLOBE, asymptotic observer with second‐order dynamics tuning—AO‐SODE) and a linear estimator (recursive least squares with variable forgetting factor—RLS‐VFF) is presented. Heterologous protein production in Pichia pastoris PAOX1 (Mut+) and PFLD1‐based systems is closely related to μ and has been addressed due to its high relevance in modern biotechnology and bioprocess engineering. μ was estimated by online gas analyses or substrate measurements, biomass, and substrate considering yield coefficients and mass balances. In simulation studies, NLOBE showed high sensitivity to tuning and initialization variables. Validation experiments demonstrated AO‐SODE performs better than the RLS‐VFF for moderate to rapid changes of μ and model parameters being known. If low changes on μ are presented, for instance, in substrate regulation, RLS‐VFF comes up as the best option, because of its reduced requirements. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2966–2979, 2012

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“…their significant variations. It was shown by Roux et al 1996 that we can adequately estimate the process of fermentation with the linear model, and at the same time keep a reasonable trade-off between the model structure complexity and Ž . its effectiveness Carrier andStephanopoulos, 1998 .…”

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confidence: 99%