No abstract

Previously conducted studies have established that surface subsidence is typically avoided by filling coal mined-out areas with solid waste. Backfilling hydraulic supports are critically important devices in solid backfill mining, whose operating performance can directly affect backfill mining efficiency. To accurately evaluate the operating performance, this paper proposes hybrid machine learning models for the operating states. An analysis of the factors that influence operating performance provides eight indices for evaluating backfilling hydraulic supports. Based on the data obtained from the Creo simulation model and field measurement, six hybrid models were constructed by combining swarm intelligent algorithms and support vector machines (SVM). Models of the SVM optimized by the modified sparrow search algorithm have shown improved convergence performance. The results show that the modified model has a prediction accuracy of 95.52%. The related evaluation results fit well with the actual support intervals of the backfilling hydraulic support.

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The unreasonable accumulation of coal gangue in mining areas has caused serious resource waste and environmental pollution. The functional utilization of coal gangue with high added value has become the key to solving the previous problem. Coal gangue has inherent advantages such as large specific surface areas and rich active components, giving rise to an excellent precursor of electrode material in electrochemical energy storage devices. Herein, we, firstly, fabricated an amorphous SiCX/SiOX electrocatalyst with an abundant oxygen vacancy by acid–alkali activation derived from coal gangue for advanced Li-O2 batteries. The in-depth experimental results coupled with an in situ characterization analysis revealed that the amorphous SiCX/SiOX layer with abundant functional groups and oxygen vacancies on the surface of the activated gangue was conducive to promote structural stability and to improve the formation/decomposition efficiency of discharged products (Li2O2). Therefore, the LOBs based on the activated coal gangue electrocatalyst delivered a low overpotential of 1.12 V, high discharge capacity of 9156 mAh g−1, and an improved cyclic stability (more than 350 h). This work can provide a new approach for the development of new functions of coal gangue.