Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models

Peng, Gongzhuang; Wang, Hongwei; Song, Xiao; Zhang, Heming

doi:10.1016/j.energy.2017.05.067

Cited by 36 publications

(13 citation statements)

References 33 publications

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“…In this study, we proposed a novel improved GM(1,1) model, DCOGM(1,1), for electricity consumption prediction. To evaluate the simulation and prediction performance of DCOGM(1,1), some compared prediction models, such as statistical analysis models (LR model and MAED [57]), computational intelligence models (BPN, RBFN and SVR), and grey prediction models (AGM(1,1) model [56], GPRM [58], NNGM(1,1) [6], DGM(1,1) [51], FGM(1,1) [50], RGM(1,1) [59], and TGM(1,1) [54]), are selected in this article. From Case 1 and Case2, we can see that the grey prediction models outperform statistical analysis models and computational intelligence models, that is the MAPEs gained by AGM(1,1) and DCOGM(1,1) are lower than those by BPN and SVR in Case 1 as shown in Table 1 and the MAPEs gained by MAED, BPN and RBFN are higher than those by GPRM, NNGM(1,1) and DCOGM(1,1) in Case 2 as indicated in Table 2.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, in this paper, we optimized the GM(1,1) model by combining data transformation for the original data sequence and combination interpolation optimization of the background value, namely DCOGM(1,1). The results of three cases suggest that the prediction performance of DCOGM(1,1) is not only better than statistical analysis models and computational intelligence models, but also better than other grey modification models, such as AGM(1,1) model [56], GPRM [58], NNGM(1,1) [6], DGM(1,1) [51], FGM(1,1) [50], RGM(1,1) [59] and TGM(1,1) [54]. Thus, DCOGM(1,1) is a promising tool for short-term electricity consumption prediction.…”

Section: Discussionmentioning

confidence: 99%

“…The detailed procedure to forecast Shanghai's total electricity consumption is as follows: Firstly, different prediction models, which are DCOGM(1,1), traditional GM(1,1)model, and four published improved GM(1,1)models, DGM(1,1) [51], FGM(1,1) [50], RGM(1,1) [59], TGM(1,1) [54], and linear regression (LR) model, are constructed separately with the data of annual total electricity consumption of Shanghai City from 2010 to 2014, then the prediction performances of those prediction models are examined and compared with the data from year 2015 and 2016, and, finally, the total electricity consumption forecasting of Shanghai city from 2017 to 2021 is predicted by the superior model.…”

Section: Modelling Procedures Of Shanghai's Electricity Consumption Fomentioning

confidence: 99%

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Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot provide sufficient information to gain satisfactory prediction accuracy. To address this problem, we propose a novel improved grey forecasting model, which combines data transformation for the original data sequence and combination interpolation optimization of the background value of the GM(1,1) model, and is therefore named DCOGM(1,1). To evaluate the simulation and prediction performance of DCOGM(1,1), two case studies are carried out. In addition, the results show that DCOGM(1,1) outperforms most existing improved grey models in terms of forecasting accuracy. Finally, DCOGM(1,1) is employed to predict the total electricity consumption of Shanghai City in China from 2017 to 2021. In addition, the results suggest that DCOGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai's electricity consumption will increase stably in the following five years. In summary, DCOGM(1,1) proposed in our study has competent exploration and exploitation ability, and could be utilized as an effective and promising tool for short-term planning for other forecasting issues with limited source data as well.The forecast accuracy of electricity consumption is affected by many factors, such as economic development [8], population [9], power facilities [10] and climate factors [11], and thus the data sequence of electricity consumption often presents to be highly nonlinear, stochastic, time-changeable,

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Modelling Procedures Of Shanghai's Electricity Consumption Fomentioning

confidence: 99%

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Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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“…Wang et al (2014) reported that applying polyethylene glycol (PEG) decreases the concentrations of readily oxidized functional groups on coal surfaces. 13,14 2 | EXPERIMENTAL 10 Moreover, Shi et al (2005) used layered double hydroxides (LDHs) as a flame retardant to interrupt the combustion diffusion by changing the material surface structure.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we established a feasible control mechanism for attenuating both coal mine accidents and environmental pollution. 13,14 2 | EXPERIMENTAL…”

Section: Introductionmentioning

confidence: 99%

Comparison of the inhibition mechanisms of five types of inhibitors on spontaneous coal combustion

Tsai¹,

Yang

Wang

et al. 2017

Int J Energy Res

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Summary Five inhibitors—Zn/Mg/Al‐CO3 layered double hydroxides (LDHs), thermosensitive hydrogel (P(NIPA‐co‐SA)), diammonium phosphate ((NH4)2HPO4), sodium phosphate (Na3PO4), and magnesium chloride (MgCl2)—commonly used to forestall the spontaneous combustion of anthracite and coke coal were investigated in this study, and the inhibition effects were quantified. According to the results of thermogravimetry, differential scanning calorimetry, Fourier transform infrared spectroscopy, and kinetic analysis, Zn/Mg/Al‐CO3‐LDHs, P(NIPA‐co‐SA), and (NH4)2HPO4 all exert substantial inhibiting effects on anthracite and coke coal. Specifically, P(NIPA‐co‐SA) was altered during the liquid‐to‐gel phase, which isolated the oxygen from the coal surface and produced an endothermic reaction that decreased the environmental temperature; this reaction further inhibited spontaneous combustion. Conversely, MgCl2 promoted a combustion reaction and reduced the apparent activation energy of coal, increasing the risk of spontaneous combustion. This study provides a reference for selecting suitable inhibitors to prevent the spontaneous combustion of coal.

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Prediction of BP neural network and preliminary application for suppression of low‐temperature oxidation of coal stockpiles by pulverized coal covering

Wan

Chu

et al. 2020

Can J Chem Eng

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We have developed a new method to suppress spontaneous combustion of coal piles by covering the surface of coal piles with pulverized coal. Experimental studies of three type of coal samples from China (YJL, CYW, and SW) with particle size ratio of 10:1 were performed to investigate the low-temperature oxidation of coal pillars. In this work, we have also demonstrated that the distributions of oxygen concentration, the temperature field, as well as the spontaneous combustion of three typical Chinese coal samples can be predicted accurately using back-propagation neural network (BPNN) by MATLAB. Pearson correlation analysis showed that temperature and oxygen concentration highly depend on the ratio of pulverized coal thickness to coal piles thickness, activation energy, void ratio, wind speed, and low-temperature oxidation time. Three-layer BPNN models with five input factors were developed to predict the low-temperature oxidation process under pulverized coal. The prediction data of BPNN are fitting better with our experimental data, which confirms that BPNN modelling can accurately predict the low temperature oxidation process of coal.

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Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models

Cited by 36 publications

References 33 publications

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Comparison of the inhibition mechanisms of five types of inhibitors on spontaneous coal combustion

Prediction of BP neural network and preliminary application for suppression of low‐temperature oxidation of coal stockpiles by pulverized coal covering

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