A Hybrid Genetic Algorithm For Byproduct Gas Scheduling In A Steel Plant

Steel industry is a class of high energy consumed industry, whose byproduct gases are viewed as important secondary fuel resource. Therefore, its reasonable usage can not only reduce the energy consumption of steel plant, increase economic efficiency and enhance the competitiveness of the enterprise, but also save the natural resources and reduce pollution on the environment. The scheduling problem should ensure a stable gas supply and the production safety, however, the gas system structure is rather complex, which involves numerous users, it is necessary to consider the whole system to model and optimize it.In this paper, a hybrid genetic algorithm based method is proposed for solving the byproduct gas scheduling in a steel plant. An optimal scheduling model is established to minimize the production cost via analyzing the system structure, the gas holder adjustment means and the use of gas features of adjustable units in this plant. To achieve the maximum economic benefit, the scheduling model meets the requirement of adjusting gas holders’ level, while considering the impacts of purchased energy, gas flow, electricity production, holders’ level deviation, as well as the differences of scheduling units using gas, the relationship between the gas flow of each unit and the gas holders’ level. The hybrid genetic algorithm is used for solving the optimal model to avoid infeasible solution in the computation process, reduce the complexity and difficulty of solving nonlinear programming, and ensure search efficiency of global solution and local solution in viable domain.To verify the effectiveness of the proposed method, a number of experiments are performed by considering the gas holders’level surplus and shortage in industrial situations by using the real data in the steel plant. The results indicate the effectiveness of the proposed method by comparing with some other algorithms.