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.

配料是烧结过程中的重要环节，它影响烧结成本和烧结矿质量。针对烧结过程中原料信息复杂、约束条件多等问题，以配料成本和TFe为优化目标，建立了烧结配料多目标优化模型。此外，提出了一种改进的多目标白鲸优化（IMOBWO）算法来解决非线性、多约束的多目标优化问题。该算法利用约束非支配准则来处理模型中的约束问题。此外，该算法采用相反的学习策略和基于角竞争和两个种群竞争策略的种群引导机制，以增强收敛性和种群多样性。某钢厂的实际配料表明，IMOBWO算法应用于矿石配料过程具有良好的收敛性，并得到均匀分布的Pareto前沿。同时，与实际配料方案相比，最优折衷方案的配料成本降低了4.3361元/t，混合物中TFe含量提高了0.0367%。因此，该方法有效平衡了成本和总铁量，有利于烧结铁矿资源的综合利用，同时保证质量。