Application of Pattern Matching in Heating Furnace – Rough Rolling Process

Abstract

Abstract Hot continuous rolling is a complex industrial process. Aiming at the problems of difficult optimization control, information island between process and strong coupling between parameters in 2250 mm rolling line of a steel plant, a parameter optimization method based on multi-level pattern matching is proposed, which is applied to the heating furnace – rough rolling link to adapt the best operation parameters in the current superior operational pattern library so as to improve the product quality of transfer bar. For the situation that the multi-level matching method cannot find the optimal operation mode, an input-output prediction model based on fuzzy neural network is established and particle swarm optimization algorithm is used until the output optimal operation parameters meet the requirements, and updates the new optimal mode to the superior operational pattern library to complete the expansion of the library. The simulation results show that the minimum relative error between the optimization results and the actual operation parameters is about 0.44%, which proves the feasibility of the pattern matching and evolution strategy, so as to achieve the purpose of improving the quality of hot rolling products and improving the enterprise efficiency.