Knowledge and mathematical programming-based optimal scheduling for byproduct gas system in steel industry

Abstract

Aiming at the optimal scheduling problem of byproduct gas system in steel industry, a knowledge and mathematical programming-based optimal scheduling method is proposed in this study. On one hand, a fuzzy model is designed to extract the expert scheduling knowledge from the historical data of the industrial process. And then, a great deal of scheduling knowledge is employed to compose a fuzzy rules base, which can be used for fuzzy inference of operation scheme with a new input. On the other hand, a mixed integer linear program (MILP) method is built to further optimize the operation scheme. Thus, a more reasoning and optimal operation scheme can be achieved with the consideration of both the expert knowledge and the mathematical programming method. Finally, a byproduct gas system of one steel industry is studied for experiments to verify the effectiveness and practicability of the proposed method.