Search results

In China there are large amounts of coal located underneath buildings. In this article the severity of the problem is studied, and the characteristics of the mining technology used for the extraction of coals under buildings are described. Based on the comprehensive analyses of the abundant literature, separated bed grouting, backfill mining, Partial Mining and homonic mining technology, are the main mining technologies that exist for extraction of these coals. At the same time, the necessary measures for protecting the buildings are also considered. In particular, the authors have analysed the development of these mining technologies and concluded that the waste filling will be the focus of development, and that the main developing technologies of the filling mining method should be further perfecting the separated bed filling, strip mining and strenthening the anti-deformation of building.

Numerous smog events have occurred in recent years in China. Their hazards in mining and industrial cities are more serious than clear days. The samples were collected in the mining and industrial city of Handan. During the smog episode, PM₁₀ and PM_2.5 concentrations reach up to 980 μg/m³ and 660 μg/m³, respectively. Under SEM (scanning electron microscope) analysis, the particles consist of soot, fly ash and minerals, which could be from coal mines, power plants, steel mills and auto exhausts. Compared with the samples collected on a clear day, the increased PM₁₀ particles are mainly composed of organic matter, especially aromatic compounds. The Pb content in PM₁₀ of the smog day reaches 507.4 ng/m³ and could be caused by vehicle emissions.

Pyrolysis for coal gas generation changes the composition, pore structure, permeability and adsorption capacity of coal. This work aims to discuss the utilization of coal pyrolysis on enhancing coalbed methane (CBM) production in the Gujiao area, Shanxi province, China.

This research was conducted mainly by the methods of thermogravimetry mass spectrometry (TG-MS) analysis, liquid nitrogen adsorption experiment and methane isothermal adsorption measurement.

The results can be concluded as that 400-700°C is the main temperature range for generating CH₄. Pore volume and specific surface area increase with increasing temperature; however, the proportion of micro pore, transition pore and macro pore has no difference. The optimum temperature for enhancing CBM production should be letter than 600°C because the sedimentation of tar and other products will occupy some pores and fissures after 600°C.

Here in, to accurately recognize the suitable maximum temperature for heating development, a method enhancing CBM production, TG-MS, was adopted to analyze the products and the weight loss of coals with different ranks in the Gujiao area at temperature of 30-1,100°C. And then the pore structure, porosity, permeability, methane adsorption capacity and thermal maturity of coals during pyrolysis were investigated with increased temperature from 30°C to 750°C. On these bases, the favorable condition for enhancing CBM production and the thermal evolution of coal were recognized.

This paper describes the organic geochemical characteristics and their roles on barium enrichment in the No. 2 Coal from Huanglong Jurassic Coalfield, China. A total of 18 bench samples were taken from Huangling Mine 2. The average content of barium (3701 mg/kg) was about 23 times higher than that of common world coals. Terrestrial higher plants were the main coal-forming parent material. Relying on the parameters of OEP, Pr/Ph and so on, there is little correlation between organic geochemical characteristics and barium enrichment. Therefore, organic material has little influence on the process of coal-forming and the enrichment of barium.

In this paper, considering a tradeoff between consumers comfort and energy efficiency, a multi-period joint energy scheduling algorithm (MPJ-ESA) based on prediction of residents energy consumption is proposed, which includes long-period preliminary sch eduling, short-period preliminary scheduling, and real-time fine-tuning scheduling. First, by analyzing historical data of energy consumption, preferred usage profile of consumers is inferred, and the dynamic comfort level is presented. Then the paper uses the wavelet neural networks (WNNs) prediction algorithm to predict the operation of the appliances which are classified into appliances with unschedulable mode and schedulable mode. Based on the energy consumption prediction and dynamic comfort level, home appliances running state are scheduled according to the prediction of renewable energy available amount and real-time pricing (RTP). The simulation results show that scheduling algorithm effectively improves the energy efficiency and enhances user satisfaction with the operation of scheduled appliances and let the consumers comfort and energy efficiency achieve a better tradeoff.