Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry
来源期刊：中南大学学报(英文版)2015年第4期
论文作者：LI Lei LI Hong-juan
文章页码：1437 - 1447
Key words：surplus gas prediction; probabilistic scheduling; iron and steel enterprise; HP filter; Elman neural network (ENN); least squares support vector machine (LSSVM); Markov chain
Abstract: To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.
LI Lei(李磊)1, LI Hong-juan(李红娟)2
(1.Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction of Ministry of Education, State Key Laboratory of Complex Non-ferrous Metal Resources Clean Utilization
(Kunming University of Science and Technology), Kunming 650093, China;
2. Quality Development Institute, Kunming University of Science and Technology, Kunming 650093, China)
Abstract:To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.
Key words:surplus gas prediction; probabilistic scheduling; iron and steel enterprise; HP filter; Elman neural network (ENN); least squares support vector machine (LSSVM); Markov chain
