Optimal proportioning of iron ore in sintering process based on improved multi-objective beluga whale optimisation algorithm

Abstract

Proportioning is an important part of sintering, as it affects the cost of sintering and the quality of sintered ore. To address the problems posed by the complex raw material information and numerous constraints in the sintering process, a multi-objective optimisation model for sintering proportioning was established, which takes the proportioning cost and TFe as the optimisation objectives. Additionally, an improved multi-objective beluga whale optimisation (IMOBWO) algorithm was proposed to solve the nonlinear, multi-constrained multi-objective optimisation problems. The algorithm uses the constrained non-dominance criterion to deal with the constraint problem in the model. Moreover, the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity. The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front. Meanwhile, compared with the actual proportioning scheme, the proportioning cost is reduced by 4.3361 ¥/t, and the TFe content in the mixture is increased by 0.0367% in the optimal compromise solution. Therefore, the proposed method effectively balances the cost and total iron, facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.