Data-driven multi-period modeling and optimization for the industrial steam system of large-scale refineries - Xu, Tiantian; Li, Tianyue; Long, Jian; Zhao, Liang; Du, Wenli (Chemical engineering science, 2023)
•A novel steam system model with periodic process steam demand is established.•The optimized model is proposed according to CO2 emission and outsourced steam.•Uncertainty of equipment failure is discretely classified into three scenarios.•Three cases of a real refinery are explored based on the proposed model. Steam system modeling and optimization contributes toward low energy consumption for large-scale industrial refineries. However, formidable challenges have been generated by steam demand variation, the optimization of CO2 emissions and outsourced steam, the uncertainty of sudden equipment failures, and the introduction of new equipment. To address this issue, this work presents a novel steam system model with periodic process steam demand. The multiple cost factors of CO2 emission treatment and outsourced steam are considered and formulated as a mixed integer nonlinear programming optimization problem. A strategy to discretize the uncertainty of equipment failure into three main scenarios is proposed. The proposed model, strategy, and introduction of new equipment are demonstrated on three cases of industrial refinery steam systems. In case 1, the optimized operating costs have reduced by 1.5%. In case 2, the optimized average operating cost is 1,135,402 CNY/h. In case 3, with the introduction of the aromatics turbocharged turbine (ATT) and the letdown valve, the optimized operating costs are 1,292,182 CNY/h and 1,316,227 CNY/h respectively, confirming the feasibility of introducing ATTs.