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Evolutionary process scheduling approach for energy cost minimization in a yeast production factory: design, simulation, and factory implementation

Evolutionary process scheduling approach for energy cost minimization in a yeast production... Energy efficiency is today the main issue for any yeast production company. Portuguese yeast companies are not an exception. Purely replacing some equipment with more energy efficient ones surely contributes to decrease the overall energy consumption, but it is not enough. A yeast production process needs to be characterized and analyzed as a distributed and resource-constrained system. To achieve that in a Portuguese yeast factory, a suitable solution to obtain a dynamic resource-constrained process scheduling was pursued. A genetic algorithm (GA) based scheduling system was used to scheduling optimization of factory’s fermentation units to minimize their specific cost at yeast production. Numerical simulations were first effectuated for calibration and validation of the yeast production model developed. A 2.29% reduction in the electricity cost per ton and per week of yeast production was achieved, which means about 7500 euros/year if the level of optimization is maintained. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy Systems Springer Journals

Evolutionary process scheduling approach for energy cost minimization in a yeast production factory: design, simulation, and factory implementation

Faria, G.; Vieira, Susana; Branco, P.
Energy Systems , Volume 10 (1) – Jun 4, 2018
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27 pages

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Energy; Energy Policy, Economics and Management; Operations Research/Decision Theory; Optimization; Energy Systems
ISSN
1868-3967
eISSN
1868-3975
DOI
10.1007/s12667-018-0275-y
Publisher site
See Article on Publisher Site

Abstract

Energy efficiency is today the main issue for any yeast production company. Portuguese yeast companies are not an exception. Purely replacing some equipment with more energy efficient ones surely contributes to decrease the overall energy consumption, but it is not enough. A yeast production process needs to be characterized and analyzed as a distributed and resource-constrained system. To achieve that in a Portuguese yeast factory, a suitable solution to obtain a dynamic resource-constrained process scheduling was pursued. A genetic algorithm (GA) based scheduling system was used to scheduling optimization of factory’s fermentation units to minimize their specific cost at yeast production. Numerical simulations were first effectuated for calibration and validation of the yeast production model developed. A 2.29% reduction in the electricity cost per ton and per week of yeast production was achieved, which means about 7500 euros/year if the level of optimization is maintained.

Journal

Energy Systems – Springer Journals

Published: Jun 4, 2018

References

  • A comparative study on applications of artificial intelligence-based multiple models predictive control schemes to a class of industrial complicated systems
    Mazinan, AH; Khalaji, AR
  • Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China
    Guo, ZC; Fu, ZX
  • Energy requirements for alternative food processing technologies—principles, assumptions, and evaluation of efficiency
    Rodriguez-Gonzalez, O
  • Food engineering at multiple scales: case studies, challenges and the future–a European perspective
    Roos, YH; Fryer, PJ; Knorr, D; Schuchmann, HP; Schroën, K; Schutyser, MA; Windhab, EJ
  • Energy efficiency opportunities in the US commercial baking industry
    Therkelsen, P; Masanet, E; Worrell, E
  • A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid
    Khadgi, P
  • Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery
    Hecker, FT; Stanke, M; Becker, T; Hitzmann, B
  • Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps
    Olszewski, P
  • Adaptation in Natural and Artificial Systems
    Holland, J
  • OCW, Lecture Notes, An introduction to genetic algorithms [PowerPoint slides]
    Weck, O
  • An Introduction to Genetic Algorithms. A Bradford Book
    Mitchell, M