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.

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 SiC_X/SiO_X 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 SiC_X/SiO_X 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.

Coal development produces large amounts of coal-based solid wastes (coal gangue, slag, fly ash) and substantial CO₂ emissions, posing severe ecological burdens on mining areas. This study integrated coal-based solid-waste cemented backfilling with CO₂ mineral sequestration to co-dispose of coal-based solid wastes and CO₂, experimentally investigating the CO₂ sequestration performance and uniaxial compressive strength (UCS) of coal-based solid-waste cemented backfill (CSCB) under mineralization curing with different CO₂ concentrations. TG-DTG, SEM-EDS, and XRD were adopted to investigate the CO₂ mineral sequestration process and the strength reduction of CSCB. The TG-DTG-based calculated CO₂ uptake of CSCB was positively correlated with the CO₂ concentration, rising by 86.9% from 2.52% to 4.71% as the concentration increased from 1.5% to 10%. In contrast, CSCB UCS decreased drastically by 88.1% from 6.05 MPa to 0.72 MPa when concentration increased from 0 to 10%. The combined microcharacterization results suggest that CO₂ reacted with alkaline substances and hydration products (CH, C-(A)-S-H gel, AFt) in CSCB to form carbonates. The UCS reduction is interpreted as arising mainly from a weakened alkaline environment (according to results and speculation), degradation of the C-(A)-S-H gel, and the AFt–CO₂ substitution reaction. Within the investigated conditions (a single mix proportion and a 14-day curing age), this study provides a reference for coal-based solid-waste backfilling coupled with CO₂ mineral sequestration; extrapolation to other mix proportions, longer curing ages, or field-scale applications requires further verification.