Title: Multi-objective optimisation of induction heating processes: methods of the problem solution and examples based on benchmark model

Authors: Paolo Di Barba; Yuliya Pleshivtseva; Edgar Rapoport; Michele Forzan; Sergio Lupi; Elisabetta Sieni; Bernard Nacke; Aleksandr Nikanorov

Addresses: Department of Electrical Engineering, University of Pavia, via Ferrata 1 – 27100 Pavia, Italy ' Department of Heat-and-Power Engineering, Samara State Technical University, Molodogvardeyskaya Str., 244, 443100, Samara, Russia ' Department of Automatics and Information Technologies, Samara State Technical University, Molodogvardeyskaya Str., 244, 443100, Samara, Russia ' Department of Industrial Engineering, University of Padua, via Gradenigo, 6/A – 35131 – Padova, Italy ' Department of Industrial Engineering, University of Padua, via Gradenigo, 6/A – 35131 – Padova, Italy ' Department of Industrial Engineering, University of Padua, via Gradenigo, 6/A – 35131 – Padova, Italy ' Institute of Electrotechnology, Leibniz Universität, Wilhelm-Busch-Str. 4, D-30167 Hannover, Germany ' Institute of Electrotechnology, Leibniz Universität, Wilhelm-Busch-Str. 4, D-30167 Hannover, Germany

Abstract: The main goal of the researches is the development of new approaches, algorithms and numerical techniques for multi-objective optimisation of design of industrial induction heating installations. A multi-objective optimisation problem is mathematically formulated in terms of the typical optimisation criteria, e.g., maximum heating accuracy and minimum energy consumption. Various mathematical methods and algorithms for multi-objective optimisation, such as Non-dominated Sorting Genetic Algorithm (NSGA-II) and optimal control alternance method, have been implemented and integrated in a user-friendly automated optimal design package. Several optimisation procedures have been tested and investigated for a problem-oriented mathematical model in a number of comparative case studies. A general comparison of the design solutions based on NSGA-II and alternance method leads to their good agreement in all investigated cases. The methodology developed is planned to be applied to more complex real-life problems of the optimal design and control of different induction heating systems.

Keywords: multi-objective optimisation; optimal design; induction heating installation; Pareto optimality; genetic algorithms; NSGA-II; alternance method; heating accuracy; energy consumption; optimal control; mathematical modelling.

DOI: 10.1504/IJMMP.2013.057072

International Journal of Microstructure and Materials Properties, 2013 Vol.8 No.4/5, pp.357 - 372

Published online: 07 Oct 2013 *

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