Multi-objective stochastic optimization of electric arc furnace process

Abstract

In this research, a stochastic multi-objective mathematical model based on material and energy flow under the system energy uncertainty is developed to simulate the EAF process and optimize the carbon dioxide emission and cost. Considering several energy-saving technologies based on the stochastic thermodynamic energy efficiency and electricity price, this research suggests an optimal cost-saving and carbon dioxide emission reducing strategy. The system uncertainty of specific electricity demand related scraps characteristics and operation conditions, with the electricity price-uncertainty, are reflected by using scenario-based method in the model. To solve the multi-objective optimization problem that minimize cost and emissions are obtained a set of pareto-optimal solutions using the ε-constraint method. The suggested model provides a trade-off relationship between the cost and emission. As the emission restrictions increase, the model suggests the utilization of a tunnel type preheater via heat recovery and use pretreated raw materials to reduce the indirect emissions by electricity use. In contrast, in the case of cost minimization for utilizing cost-effective fossil fuel instead of electricity as the system energy requirement, an oxy-fuel burner and vertical shaft furnace type of preheater was proposed. The problem was formulated as a mixed-integer linear programming model (MILP) and solved using the solver CPLEX on the optimization software GAMS.