Optimization method for multi-process energy consumption in iron and steel enterprises under the coordinated use of comprehensive energy

Abstract

Sustainable development goals, such as energy conservation, emission reduction, cost reduction, and efficiency enhancement, present pressing challenges for the iron and steel industry. Process optimization technology for efficient production, energy conservation, and consumption reduction in the steel manufacturing process is based on the coordination of material and energy flow. Therefore, this article takes the production process of a converter steelmaking plant as the object, considers the regulatory requirements of multi-process and multi constraint operation optimization under the cooperation of production organization and energy conservation and consumption reduction, and it explores a production scheduling optimization method for steelmaking plants oriented towards material flow and energy flow synergy. Based on the characteristics of the problem space, this study suggests a hybrid genetic algorithm with local search (MOHGALS) that employs both standard and advanced evolution techniques. Tests on concrete problems demonstrate that the MOHGALS approach yields a superior set of multi-objective Pareto schemes. Additionally, random-generated test cases were used to evaluate the algorithm’s performance. The outcomes show that MOHGALS performs better than competing algorithms on multi-objective evaluation indicators, and that the suggested improved evolution approach may effectively boost the algorithm’s performance.