Dynamic Multi-objective Operation Optimization of Blast Furnace Based on Evolutionary Algorithm
摘要
Blast furnaces play a critical role in the steel industry, and their operational optimization is crucial for energy conservation and emissions reduction. This paper examines the impact of changes in operational conditions on blast furnace performance. We propose a dynamic multi-objective optimization algorithm based on multiple short time series (MT-DC-RVEA) to solve the constructed dynamic multi-objective operational optimization model for blast furnaces. Experimental results validate the effectiveness of the proposed algorithm in solving the operational optimization model for blast furnace operations.