Next Article in Journal
A Comprehensive Study of Energy Conservation in Electric-Hydraulic Injection-Molding Equipment
Previous Article in Journal
A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1763; doi:10.3390/en10111763

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

Chemical Engineering Department, State University of Maringá, Colombo Av. 5790, 87020-900 Maringá, Brazil
Federal Institute of Education, Science and Technology—Currais Novos Campus, Manoel Lopes Filho St., 773, 59380-000 Currais Novos, Brazil
Author to whom correspondence should be addressed.
Received: 6 July 2017 / Revised: 2 September 2017 / Accepted: 15 September 2017 / Published: 2 November 2017
(This article belongs to the Section Energy Sources)
View Full-Text   |   Download PDF [718 KB, uploaded 2 November 2017]   |  


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. View Full-Text
Keywords: biofuels; process control; optimization; ethanol; fermentation; bioenergy biofuels; process control; optimization; ethanol; fermentation; bioenergy

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Freitas, H.F.S.; Olivo, J.E.; Andrade, C.M.G. Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques. Energies 2017, 10, 1763.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top