Control of continuous fed-batch fermentation process using neural network based model predictive controller

Kiran, Anil; Jana, Asim Kumar

doi:10.1007/s00449-009-0306-0

Cited by 18 publications

(14 citation statements)

References 41 publications

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“…In the literature, the majority of the applications of MPC to fermentation systems have been based on simulation. These include both fed-batch [31,32] and continuous [33][34][35] systems, all of which used some form of nonlinear MPC or neural network. Though academic research continues to develop the theory and algorithms for MPC, there exists a wealth of experience that can be applied to biorefining from related industries.…”

Section: Advanced Process Controlmentioning

confidence: 99%

Use of Raman spectroscopy for continuous monitoring and control of lignocellulosic biorefinery processes

Ewanick

Schmitt

Gustafson

et al. 2014

Pure and Applied Chemistry

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The production of fuels and chemicals from lignocellulosic biomass demands efficient processes to compete with fossil fuel-derived products. Key biorefinery processes, such as enzymatic hydrolysis of cellulose and microbial fermentation, can be monitored by advanced sensors in real time, providing information about reactant and product concentration, contamination, and reaction progress. Spectroscopic techniques such as Raman spectroscopy provide a means of quickly and accurately assessing many types of reaction mixtures non-destructively, in real time, and with no costly sample preparation and analysis time. Raman spectroscopy techniques have been developed to accurately quantify a number of compounds present in lignocellulosic processes, and methods have been developed to overcome the presence of fluorescent compounds that can increase the spectral background. Online Raman sensors also can provide the feedback measurements necessary for advanced process controls (APCs). Specifically, model predictive control, a common APC used extensively throughout similar processing industries, is especially well suited for ensuring optimal production of bio-based chemicals from lignocellulosic material.Article note: A collection of invited papers based on presentations at the 2 nd Brazilian Symposium on Biorefineries (II SNBr), Brasília, Brazil, 24-26 September 2013.

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Section: Advanced Process Controlmentioning

confidence: 99%

Use of Raman spectroscopy for continuous monitoring and control of lignocellulosic biorefinery processes

Ewanick

Schmitt

Gustafson

et al. 2014

Pure and Applied Chemistry

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“…Further, RQ based control cannot be applied to all strains as shown by Nagamori et al (2013). Also, RQ-based control is an indirect control since the glucose concentration in the culture is not tightly regulated as demonstrated by Kiran and Jana (2009). Hence, controllers that directly influence measured variables such as substrate and dissolved oxygen concentration simultaneously, the major drivers of the yeast metabolism, are more desirable.…”

Section: Introductionmentioning

confidence: 95%

Maximizing biomass concentration in baker’s yeast process by using a decoupled geometric controller for substrate and dissolved oxygen

Chopda

Rathore

Gomes

2015

Bioresource Technology

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“…More recently, regarding the bioprocess modelling issues, a combined adaptive neuro-fuzzy modelling strategy is proposed in [16], and a neural and hybrid modelling procedure in [25]. Also, some NNs-based control strategies were developed in [14], where a NNs feedback linearizing strategy for bioprocess control is designed, and in [26] a model predictive neural controller for a fedbatch bioprocess is studied.…”

Section: Introductionmentioning

confidence: 99%

Neural networks-based adaptive control for a class of nonlinear bioprocesses

Petre

Selişteanu

Şendrescu

et al. 2009

Neural Comput & Applic

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The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and timevarying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input-output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

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Control of continuous fed-batch fermentation process using neural network based model predictive controller

Cited by 18 publications

References 41 publications

Use of Raman spectroscopy for continuous monitoring and control of lignocellulosic biorefinery processes

Use of Raman spectroscopy for continuous monitoring and control of lignocellulosic biorefinery processes

Maximizing biomass concentration in baker’s yeast process by using a decoupled geometric controller for substrate and dissolved oxygen

Neural networks-based adaptive control for a class of nonlinear bioprocesses

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