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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Optimisation of a fermentation process for butanol production by particle swarm optimisation (PSO)

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Author(s):
Mariano, Adriano Pinto [1] ; Borba Costa, Caliane Bastos [1] ; de Angelis, Dejanira de Franceschi [2] ; Maugeri Filho, Francisco [3] ; Pires Atala, Daniel Ibraim [3] ; Wolf Maciel, Maria Regina [1] ; Maciel Filho, Rubens [1]
Total Authors: 7
Affiliation:
[1] Univ Estadual Campinas, Lab Optimizat Design & Adv Control, LOPCA, Sch Chem Engn, UNICAMP, Campinas, SP - Brazil
[2] Sao Paulo State Univ, Dept Biochem & Microbiol, Inst Biosci, UNESP, Sao Paulo - Brazil
[3] Univ Estadual Campinas, Lab Bioproc Engn, Sch Food Engn, UNICAMP, Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY; v. 85, n. 7, p. 934-949, JUL 2010.
Web of Science Citations: 10
Abstract

BACKGROUND: The performance of three particle swarm optimisation (PSO) algorithms was assessed in relation to their capability to optimise an alternative fermentation process for the production of biobutanol. The process consists of three interconnected units: fermentor, cell retention system and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The dynamic behaviour of the process was described by a non-linear mathematical model. Four constrained optimisation problems were formulated concerning the operation and design of flash fermentation: (1) maximisation of butanol productivity; (2) maximisation of substrate conversion; (3) and (4) adjustment of operating conditions in the face of problems of fluctuations in the quality of the agricultural raw material and changes in the kinetics of the microorganisms. RESULTS: The design and operation of the flash fermentation process based on the optimisation of productivity, instead of substrate conversion, resulted in a smaller fermentor and provided satisfactory values of operating conditions able to overcome problems of variations in the glucose concentration in the raw material and changes in kinetics. CONCLUSIONS: The differences among the PSO algorithms, i.e. the velocity equation and parameters values, had significant effects on the optimisation, the best results being obtained with the original velocity equation with the inertia weight decreasing linearly with each iteration. The PSO algorithms obtained solutions that obeyed constraints, demonstrating that a constraint handling method originally developed for genetic algorithms can be applied successfully to PSO algorithms. (C) 2010 Society of Chemical Industry (AU)