Model-based Multi-objective Optimisation of Reheating Furnace Operations using Genetic Algorithm

Yukun Hu, Chee Tan, Jonathan Broughton, Paul Roach, Liz Varga

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An effective optimisation strategy for metal reheating processes is crucial for the economic operation of the furnace while supplying products of a consistent quality. An optimum reheating process may be defined as one which produces heated stock to a desired discharge temperature and temperature uniformity while consuming minimum amount of fuel energy. A strategic framework to solve this multi-objective optimisation problem for a large-scale reheating furnace is presented in this paper. For a given production condition, a model-based multi-objective optimisation strategy using genetic algorithm was adopted to determine an optimal temperature trajectory of the bloom so as to minimise an appropriate cost function. Definition of the cost function has been facilitated by a set of fuzzy rules which is easily adaptable to different trade-offs between the bloom desired discharge temperature, temperature uniformity and specific fuel consumption. A number of scenarios with respect to these trade-offs were evaluated and the results suggested that the developed furnace model was able to provide insight into the dynamic heating behaviour with respect to the multi-objective criteria. Suggest findings that current furnace practice places more emphasis on heated product quality than energy efficiency.
Original languageEnglish
Title of host publicationEnergy Procedia
Number of pages9
ISBN (Electronic)1876-6102
Publication statusPublished - 31 Jan 2018
Event9th International Conference on Applied Energy: Energizing the future - Cardiff University, Cardiff, United Kingdom
Duration: 21 Aug 201724 Aug 2017

Publication series

NameEnergy Proceedia
ISSN (Print)1876-6102


Conference9th International Conference on Applied Energy
Abbreviated titleICAE2017
Country/TerritoryUnited Kingdom


  • zone model
  • reheating furnace
  • multi-objective optimisation
  • genetic algorithm


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