Comparative Performance of Decoupled Input–Output Linearizing Controller and Linear Interpolation PID Controller: Enhancing Biomass and Ethanol Production in Saccharomyces cerevisiae

A decoupled input–output linearizing controller (DIOLC) was designed as an alternative advanced control strategy for controlling bioprocesses. Simulation studies of its implementation were carried out to control ethanol and biomass production in Saccharomyces cerevisiae and its performance was compared to that of a proportional–integral–derivative (PID) controller with parameters tuned according to a linear schedule. The overall performance of the DIOLC was better in the test experiments requiring the controllers to respond accurately to simultaneous changes in the trajectories of the substrate and dissolved oxygen concentration. It also exhibited better performance in perturbation experiments of the most significant parameters qS,max, qO2,max, and ks, determined through a statistical design of experiments involving 730 simulations. DIOLC exhibited a superior ability of constraining the process when implemented in extreme metabolic regimes of high oxygen demand for maximizing biomass concentration and low oxygen demand for maximizing ethanol concentration.

[1]  Inferential control of the specific growth rate in fed-batch cultivation processes , 2001, Biotechnology Letters.

[2]  G. Klinzing,et al.  Competition for mixed substrates by microbial populations , 1977, Biotechnology and bioengineering.

[3]  Fernando José Von Zuben,et al.  Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process , 2009, Eng. Appl. Artif. Intell..

[4]  John P. Barford,et al.  A mathematical model for the aerobic growth of Saccharomyces cerevisiae with a saturated respiratory capacity , 1981 .

[5]  J. Nielsen,et al.  Metabolic Engineering of Saccharomyces cerevisiae , 2000, Microbiology and Molecular Biology Reviews.

[6]  R. Simutis,et al.  Automatic control of the specific growth rate in fed-batch cultivation processes based on an exhaust gas analysis , 1996 .

[7]  Anurag Rathore,et al.  Use of the design‐of‐experiments approach for the development of a refolding technology for progenipoietin‐1, a recombinant human cytokine fusion protein from Escherichia coli inclusion bodies , 2009, Biotechnology and applied biochemistry.

[8]  B Sonnleitner,et al.  Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis , 1986, Biotechnology and bioengineering.

[9]  D. Kompala,et al.  Cybernetic model of the growth dynamics of Saccharomyces cerevisiae in batch and continuous cultures. , 1999, Journal of biotechnology.

[10]  Tore Hägglund,et al.  Advanced PID Control , 2005 .

[11]  Alain Vande Wouwer,et al.  Robust adaptive control of yeast fed-batch cultures , 2008, Comput. Chem. Eng..

[12]  R. Plackett,et al.  THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS , 1946 .

[13]  R. H. De Deken,et al.  The Crabtree Effect: A Regulatory System in Yeast , 1966 .

[14]  Hisbullah,et al.  Comparative evaluation of various control schemes for fed-batch fermentation , 2002 .

[15]  Carlos A Cardona,et al.  Fuel ethanol production: process design trends and integration opportunities. , 2007, Bioresource technology.

[16]  Asim Kumar Jana,et al.  Control of continuous fed-batch fermentation process using neural network based model predictive controller , 2009, Bioprocess and biosystems engineering.

[17]  C. Cannizzaro,et al.  Control of yeast fed-batch process through regulation of extracellular ethanol concentration , 2004, Bioprocess and biosystems engineering.

[18]  F Rodríguez-Acosta,et al.  Non-linear optimization of biotechnological processes by stochastic algorithms: application to the maximization of the production rate of ethanol, glycerol and carbohydrates by Saccharomyces cerevisiae. , 1999, Journal of biotechnology.

[19]  A S Rathore,et al.  Use of computational fluid dynamics as a tool for establishing process design space for mixing in a bioreactor , 2012, Biotechnology progress.

[20]  W. Seghezzi,et al.  Regulation of glucose metabolism in growing yeast cells , 1992 .

[21]  James Gomes,et al.  Simultaneous dissolved oxygen and glucose regulation in fed-batch methionine production using decoupled input-output linearizing control , 2009 .

[22]  Celso Axelrud,et al.  Industrial application of nonlinear model predictive control technology for fuel ethanol fermentation process , 2009, 2009 American Control Conference.

[23]  F. Kargı,et al.  Bioprocess Engineering: Basic Concepts , 1991 .

[24]  C L Cooney,et al.  Computer‐aided material balancing for prediction of fermentation parameters , 1977, Biotechnology and bioengineering.

[25]  Kapil G. Gadkar,et al.  On-line adaptation of neural networks for bioprocess control , 2005, Comput. Chem. Eng..

[26]  Aurélie Hermant,et al.  A high-throughput protein refolding screen in 96-well format combined with design of experiments to optimize the refolding conditions. , 2011, Protein expression and purification.

[27]  T. Egli,et al.  Simultaneous utilization of methanol–glucose mixtures by Hansenula polymorpha in chemostat: Influence of dilution rate and mixture composition on utilization pattern , 1986, Biotechnology and bioengineering.

[28]  Andrzej Kasperski,et al.  Optimization of pulsed feeding in a Baker's yeast process with dissolved oxygen concentration as a control parameter , 2008 .

[29]  K Y San,et al.  The design of controllers for batch bioreactors , 1988, Biotechnology and bioengineering.

[30]  O. Käppeli,et al.  An Expanded Concept for the Glucose Effect in the Yeast Saccharomyces uvarum: Involvement of Short- and Long-term Regulation , 1983 .

[31]  C. A. Alzate,et al.  Importance of stability study of continuous systems for ethanol production. , 2011 .

[32]  Reza Eslamloueyan,et al.  OPTIMIZATION OF FED-BATCH RECOMBINANT YEAST FERMENTATION FOR ETHANOL PRODUCTION USING A REDUCED DYNAMIC FLUX BALANCE MODEL BASED ON ARTIFICIAL NEURAL NETWORKS , 2011 .

[33]  Jorge Alberto Vieira Costa,et al.  The role of biochemical engineering in the production of biofuels from microalgae. , 2011, Bioresource technology.