Process optimization based on multi-objective optimization model for coking plant production

In coking plant production process of an iron and steel enterprise, the target flue temperature, gas collector pressure and coking time are set by human experience. Itpsilas difficult to adjust them timely according to different conditions, so the anticipant integrated production target canpsilat be reached. In order to solve this problem, a basic optimization idea is proposed, which is with the maximum of coke yield and minimum of energy consumption as the optimization objective and with coke quality and technological requirements as the constraint conditions. Firstly, the multiple linear regression and improved BP neural network methods are used to build a multi-objective optimization model with nonlinear inequality constraints for coking plant production process. Then, the linear weighted sum method and GRG combinatorial algorithm are applied to resolve the multi-objective optimization problem, so as to obtain the optimized target values of flue temperature, gas collector pressure and coking time. Actual application results show that the optimized result by method proposed in this paper can better meet the enterprise needs, and the industrial application effect is good.