Function Value-Based Multi-Objective Optimisation of Reheating Furnace Operations Using Hooke-Jeeves Algorithm

Bo Gao, Chunsheng Wang, Yukun Hu, C. K. Tan, Paul Alun Roach and Liz Varga
Additional contact information
Bo Gao: School of Information Science and Engineering, Central South University, Changsha 410083, China
Chunsheng Wang: School of Information Science and Engineering, Central South University, Changsha 410083, China
Yukun Hu: Complex Systems, School of Management, Cranfield University, Bedford MK43 0AL, UK
C. K. Tan: Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK
Paul Alun Roach: Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK
Liz Varga: Complex Systems, School of Management, Cranfield University, Bedford MK43 0AL, UK

Energies, 2018, vol. 11, issue 9, 1-18

Abstract: Improved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO 2 emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature.

Keywords: reheating furnace; zone model; multi-objective optimisation; Hooke-Jeeves algorithm; artificial neural network (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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