Investigation on Energy-Effectiveness Enhancement of Medium-Frequency Induction Furnace Based on an Adaptive Chaos Immune Optimization Algorithm with Mutative Scale

Abstract

Based on the chaos algorithm and immune algorithm theory, an adaptive chaotic immune optimization algorithm (ACIOA) with a mutative scale was proposed and subsequently validated by the experiment result in this paper, and then the adaptive chaotic immune optimization algorithm with mutative scale was applied to investigate the performance characteristics of the medium-frequency induction furnace. The obtained results include the effects on the performance characteristics of a medium-frequency induction furnace of the diameter of the heated cylindrical material, the thickness of the crucible wall, the fullness degree of the induction coil, the ratio of diameter to current penetration depth, and the power frequency. The results showed that the optimization algorithm could continuously modify the variable search space and take the optimal number of cycles as the control index to carry out the search. In addition, the suitable ratio of diameter to current penetration depth was between 3.5 and 6.0, and was beneficial to the improvements of in power factor and thermal efficiency. This method had the characteristics of small calculation delay, high anti-noise ability, and high detection rate. Moreover, the maximum errors of KFF, Random, and PSO were 6.4%, 6.2%, and 5.4%, respectively. The improved method had good estimation accuracy and an excellent global optimization. Meanwhile, the suitable ratio of diameter to current penetration depth, the thickness of crucible wall, and power frequency were beneficial to the improvements in power factor and thermal efficiency. Thus, the finding is helpful as a guide to determining the design of a medium-frequency induction furnace, which may be of interest for improvements in performance under different operating conditions.