Abstract
In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling.
Highlights
The iron and steel industry is an energy-intensive industry
The recovery and utilization of the byproduct gas are of great significance to achieving energy saving and emission reduction for iron and steel enterprises
We focus on byproduct gas system optimization scheduling considering prediction uncertainties
Summary
The iron and steel industry is an energy-intensive industry. According to statistics [1], it takes up more than 15% of China’s total energy demand. First developed a mixed integer linear programming (MILP) model to optimize the gas scheduling by considering the stability of the byproduct gas system. In 2013, Sun et al [9,10] proposed a nonlinear mathematical programming model for byproduct gas scheduling by considering the change of boiler efficiency. De Oliveira Junior et al [13] improved the optimal scheduling model of byproduct gas system and proposed the rule-based weights determination method. MILP is an effective method to solve the optimal scheduling problem of byproduct gas. We focus on byproduct gas system optimization scheduling considering prediction uncertainties. The generation and the consumption amount of byproduct gas are expressed as a fuzzy variable, and the fuzzy optimal scheduling model of the gas system is established.
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