Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Hanniel Ferreira Sarmento de Freitas, José Eduardo Olivo and Cid Marcos Gonçalves Andrade
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Hanniel Ferreira Sarmento de Freitas: Chemical Engineering Department, State University of Maringá, Colombo Av. 5790, 87020-900 Maringá, Brazil
José Eduardo Olivo: Federal Institute of Education, Science and Technology—Currais Novos Campus, Manoel Lopes Filho St., 773, 59380-000 Currais Novos, Brazil
Cid Marcos Gonçalves Andrade: Federal Institute of Education, Science and Technology—Currais Novos Campus, Manoel Lopes Filho St., 773, 59380-000 Currais Novos, Brazil

Energies, 2017, vol. 10, issue 11, 1-23

Abstract: Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

Keywords: biofuels; process control; optimization; ethanol; fermentation; bioenergy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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