Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models. (1st August 2017)

• Record Type: Journal Article
• Authors: Peng, Gongzhuang; Wang, Hongwei; Song, Xiao; Zhang, Heming
• Is Part Of: Energy. Volume 132(2017)
• Page Start: 269
• Page End: 279
• Publication Date: 2017-08-01
• Subjects: Coal management -- Spontaneous combustion prevention -- Grey model -- Optimization algorithm; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.05.067
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Intelligent coal stockpiles management system is significant for the next-generation cleaner power plants. Prevention of spontaneous combustion is a key issue for such a system, both in economic and environmental terms. As many factors can influence the self heating process of coal such as moisture and ash in coal, temperature distribution and stockpiles' shapes, the remaining ignition time is developed as an aggregative indicator to measure the tendencies of spontaneous coal combustion. Using this value, the grey models have been applied to forecast spontaneous combustion and their performances are good for systems with insufficient information. However, the forecasting accuracy of these models still needs to be improved. Therefore, the ABC-RGM(1, 1) model is proposed in this work based on the rolling-GM(1, 1) and the Artificial Bee Colony (ABC) optimization algorithm, which has been applied to the management system of a 4 × 600 MW power plant. The computational experiments show that the ABC-RGM(1, 1) model achieves better performance than the other popular grey models and accuracy of forecast is greatly improved especially for short-term forecasts. Such an accurate model is highly important and useful for intelligent coal management systems which can improve decision making and reduce risk. Highlights: A novel CPS framework for intelligent coal management is proposed. WSN and accurate geometric data for estimating heat accumulation is applied. An aggregative indicator can measure the combustion tendency accurately. The rolling GM(1, 1) optimized by ABC algorithm improves the prediction accuracy.