A Multicriteria Evaluation and Cascaded Optimization Framework for Integrated Energy System of Steel Industry

Abstract

Integrated Energy System (IES) involving coal, by-product gas, power, etc., plays a pivotal role in steel industry, which greatly requires reasonable utilization scheme for avoiding extensive energy consumption and intensive environment pollution. While due to the complex coupling relationship among the multiple energy and the contradiction existed between the optimization of global IES and local sub-system, an effective solution for this important practical problem is still highly required in real-world application. As such, a multi-criteria evaluation and cascaded optimization framework is proposed in this study. In order to make a comprehensive evaluation, criteria including global energy consumption and local gas emission are respectively evaluated from the perspective of both the entire IES and the by-product gas subsystem. Then, a cascaded optimization model is accordingly established, where a Reinforcement Learning based cascaded algorithm is also designed for efficiently and effectively acquiring an optimal trade-off as the final solution. The following experiments carried out on practical industrial data clearly demonstrated that the proposed framework gives scientifically reasonable as well as practically available energy utilization scheme, which is beneficial for energy-saving and emission reduction in practical application.