Optimization of Process Parameters for Medium and Thick Plates to Balance Energy Saving and Mechanical Performance

As an important basic material for modern industry, the performance and production energy consumption of medium and thick plates have an important impact on engineering quality, industry technological progress and economic benefits. However, traditional process parameter adjustment relies on manual experience, which is difficult to meet the dual needs of efficient production and energy conservation and emission reduction. This paper focuses on the energy consumption optimization problem in the production process of medium and thick plates. Under the premise of meeting the mechanical property constraints, a data-driven process parameter optimization method is proposed. Firstly, a comprehensive energy consumption prediction model for medium and thick plates is established. Secondly, based on historical data and knowledge, a data set covering chemical composition, physical parameters and process parameters is constructed, and a mechanical property prediction model is developed to achieve the prediction of actual performance. On this basis, the energy consumption minimization problem that satisfies mechanical property constraints is modeled as a constrained optimization problem, and a data-inspired initialized particle swarm optimization algorithm is designed to improve the global search capability and local convergence efficiency. Experimental results confirm that the proposed model provides more stable and accurate prediction of mechanical properties than conventional Random Forest and XGBoost models. Furthermore, compared with standard PSO, GA, SA, and ACO algorithms, the data-inspired initialized particle swarm optimization shows faster convergence and better energy-saving performance, demonstrating the overall effectiveness and practical potential of the proposed framework.