Granular-Causality-Based Byproduct Energy Scheduling for Energy-Intensive Enterprise

Abstract

The energy-intensive enterprises (EIEs), such as iron and steel enterprises, account for a significant part of the total energy consumption in society. The Linz–Donawitz converter gas (LDG) is a kind of crucial byproduct energy resource recycled during the steelmaking process, and its reasonable scheduling can effectively reduce the LDG emission and increase its efficiency. In this study, a granular-causality-based scheduling approach for the LDG system in steel industry is proposed. A granular causality technique is modeled to confirm the casual relationship of the LDG system based on the discontinuous production characteristic, in which a causality diagram is established and the phase space of the training sample is reconstructed to improve the prediction accuracy. Then, a multioutput least-square support vector machine model is constructed for the prediction of the gas tank levels. In order to consider the impact of multiple solutions on the scheduling result in a period of time, a scheduling objective function that combines the economy criterion and the safety one is designed and optimized by a modified particle swarm optimization (PSO) algorithm. The validation experiments using real-world data coming from the energy data center of a steel plant are carried out, and the results indicate that the proposed method exhibits reliable performance. Moreover, an application software system based on the proposed method is developed and implemented, which demonstrates the applicability of the proposed approach. Note to Practitioners —Given that the steelmaking process is a discontinuous one and the LDG system can hardly be described by a physical or mechanism-based model, its energy scheduling works is usually operated by the manual method currently, which leads to low accuracy and a waste of energy. Since a large number of real-time data had been accumulated by the existing SCADA system implemented in most steel plants, a data-driven long-term scheduling approach is proposed in this study. The proposed method aims at the long-term scheduling of the LDG system in steel industry, and the acquisition frequency of the real-time data is 1 min. The application system designed on the basis of the proposed method can provide effective scheduling solutions. Furthermore, it is necessary to ensure the data integrity and reliability via data imputation and filtering methods since there may exist some missing data or outliners in the acquired data from the SCADA system onsite. This study avoids the redundant introduction of such preliminary preprocessing methods for the sample data.