Multi‐Objective Optimization of Slab Heating Process in Walking Beam Reheating Furnace Based on Particle Swarm Optimization Algorithm

The unreasonably high heating process temperature of slab in furnace leads to many heating defects. To avoid these, a multi-objective optimization method is proposed for furnace temperature setting based on particle swarm optimization (PSO) algorithm. A 2D model of the finite difference scheme, in which the thickness and width were unequally partitioned, is investigated. The distance between two neighbouring nodes increased with the thickness and width, and the effects of a more detailed grid on the surface or side are observed along with influence of a rough grid in the core. Then, a multi-objective optimization function of the temperature setting, from which energy consumption and the oxidation and burning loss should be minimized, is established. The PSO algorithm is implemented to calculate the optimal value of the multi-objective optimization function. The application results show that average temperature differences at the quarter and midpoint thicknesses between the predicted model and measured values decrease from 53.4 to 8.5 degrees C and 43.6 to 11.4 degrees C, respectively; furthermore, the average oxidation and burning loss rate decrease from 0.93% to 0.79%. Average energy consumption decreases from 1.57 to 1.33 GJ t(-1), thereby considerably reducing the energy consumption of the reheating furnace and minimizing the production cost.