Multiobjective Operation Optimization of Reheating Furnace based on Data Analytics

Abstract Reheating furnace consumes a large amount of energy in the hot rolling stage of steel production, and has a significant impact on the product quality of hot rolling. To guarantee the stability of product quality, the operation optimization model of the reheating furnace is established. The operation optimization model aims to achieve optimal heating temperature settings for different slab specifications and different element contents, so that slab heating quality, heating temperature setting and slab heating time can be optimized simultaneously. To address this problem, an improved MOEA/D-DQN algorithm is developed according to the defined state and reward, in which the adaptive selection of differential mutation operators based on reinforcement learning is proposed to improve the search ability and robustness of the algorithm. Based on benchmark and practice, the experimental results show that MOEA/D-DQN algorithm can effectively solve the operation optimization problem of heating furnace.