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Experimental optimization of a real time fed‐batch fermentation process using Markov decision process
Author(s) -
Saucedo Victor M.,
Karim M. Nazmul
Publication year - 1997
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/(sici)1097-0290(19970720)55:2<317::aid-bit9>3.0.co;2-l
Subject(s) - mathematical optimization , markov decision process , process (computing) , nonlinear system , time horizon , markov chain , production (economics) , computer science , fermentation , markov process , batch processing , batch production , process engineering , biochemical engineering , mathematics , chemistry , engineering , economics , machine learning , statistics , physics , food science , organic chemistry , quantum mechanics , programming language , macroeconomics , operating system
This article describes a methodology that implements a Markov decision process (MDP) optimization technique in a real time fed‐batch experiment. Biological systems can be better modeled under the stochastic framework and MDP is shown to be a suitable technique for their optimization. A nonlinear input/output model is used to calculate the probability transitions. All elements of the MDP are identified according to physical parameters. Finally, this study compares the results obtained when optimizing ethanol production using the infinite horizon problem, with total expected discount policy, to previous experimental results aimed at optimizing ethanol production using a recombinant Escherichia coli fed‐batch cultivation. © 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55 : 317–327, 1997.