Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM.

In response to the problems of developed rock fractures and unsatisfactory bench blasting effects in open-pit coal mines in Xinjiang, China, By using on-site research, theoretical analysis, numerical simulation and other methods, the main influencing factors of burnt rock blasting are analyzed, and the impact of joint cracks in burnt rock on its blastability is quantitatively evaluated and graded. Research shows: the main influencing factors on the blastability of burnt rock are the density of original rock joint fractures, the length of original rock joint fractures, the radius of crack propagation after blasting, and the peak blasting stress. We have established quantitative mathematical models for various influencing factors, established a classification evaluation model and evaluation standards for the blastability of burnt rocks in Xinjiang region. The comprehensive evaluation index G of blastability is divided into five levels: Level I (G > 20.7) extremely easy to blast, Level II (G = 16.7–20.7) easy to blast, Level III (G = 12.7–16.6) difficult to blast, Level IV (G = 8.7–12.6) difficult to blast, and Level V (G < 8.7) extremely difficult to blast. And a study was conducted on the blastability evaluation and classification of burnt rocks in six open-pit coal mines in Xinjiang region, and the results of blastability classification were obtained: open-pit mine 1: G = 13.3 is level III difficult to blast, open-pit mine 2: G = 13.0 is level III difficult to blast, open-pit mine 3: G = 13.3 is level III difficult to blast, open-pit mine 4: G = 13.1 is level III difficult to blast, open-pit mine 5: G = 12.8 is level III difficult to blast, and open-pit mine 6: G = 12.5 is level IV difficult to blast.

The ore mining sites commonly experience slope instability, which is causing concern for the workers’ safety and the operation’s stability. Considering the Ziluoyi iron ore mining site as a case study, uniaxial compression strength and shear tests are performed on the lower disk peripheral rock, ore body, and upper disk peripheral rock, leading to the extraction of compressive strength and elastic modulus (lower disk: 77.7 MPa–9.093 GPa; ore body: 19.6 MPa–4.619 GPa; upper disk: 55.47 MPa–5.573 GPa). Further, using the Hoek–Brown criterion in conjunction with the Moher–Columb criterion, the cohesion and internal friction angle of various samples are appropriately determined (lower disk: 21.124 MPa–35.614°; ore body: 21.66 MPa–26.5°; upper disk: 42.916 MPa–25.743°). By employing an appropriate rock mass reduction model, its constants (m and s) and RMR score for the consisting layers (schist layer and ore body) of the three understudy slopes (Slope#1–Slope#3) are evaluated. An effective algorithm based on the Morgenstern–Price method is employed and its effectiveness is also proved by comparing its factor of safety results with those of Geo-Slope for various slope structures subjected to different loads. Subsequently, the stability analyses of various slopes are methodically examined by predicting the factor of safety values under multiple types of loading (i.e., self-weight + groundwater, slope self-weight + groundwater + blasting vibration force, and self-weight + groundwater + seismic force). The factor of safety values of the slopes pertinent to Slope#1/Slope#2/Slope#3 subjected to the above loading conditions are obtained as 1.354/1.273/1.188, 1.326/1.239/1.169, and 1.321/1.232/1.162, respectively. Upon comparing these results with those of a valid homeland specification, it becomes clear that the results are able to meet safety standards and ensure efficient mine operation.

To address key challenges during the capacity expansion of the Baoqing Chaoyang Open-Pit Coal Mine from 7 to 11 Mt/a—such as insufficient working-line length, excessive advancing intensity, poor coordination between mining zones, elevated internal dump slope risks, and constrained dumping capacity—this study develops a zoning optimization method based on Unascertained Measure Theory (UMT). A working-line optimization model, constructed from coal seam thickness, density, production targets, and advancing dynamics, indicates a rational length range of 1350–2050 m at 11 Mt/a. Guided by coal seam occurrence and the original layout, the initial longitudinal-mining area is reorganized into four transverse primary zones. An eight-dimensional evaluation index system is established, covering geological, geotechnical, hydrogeological, topographic, engineering, economic, environmental, and social factors. Combined weighting using the Analytic Hierarchy Process and the Entropy Weight Method, together with multiplicative normalization, yields a hybrid subjective–objective weighting set. On this basis, four-level UMT evaluation functions and an evaluation matrix are constructed, and comprehensive assessments are performed using a confidence recognition criterion. The results show that Scheme II achieves the best overall performance in terms of economic efficiency, transportation, safety, and resource utilization, with a V₁-level confidence of 0.7058, significantly higher than the other schemes. Under Scheme II, the four zones provide 971.2 Mt of recoverable raw coal, an average stripping ratio of 5.8, and a maximum service life of 34.3 years. The proposed integrated framework of working-line design, mining-zone zoning, and uncertainty-based evaluation offers a practical decision-support tool for capacity expansion planning in large-scale open-pit coal mines.

Coal-based solid waste (CBSW; fly ash, weathered coal, coal gangue) from open-pit coal mining in extremely cold regions differentially releases salinity/alkalinity, heavy metals and acids, altering surrounding black-soil properties and reshaping its bacterial communities. We compared healthy farmland black soil (CK) with three dumping treatments—fly ash (FA), weathered coal (WC) and coal gangue (CG) (six replicates each; n = 24)—using full-length 16S rRNA sequencing (PacBio CCS), physicochemical, phospholipid fatty acid and enzyme-activity analyses. The wastes showed contrasting stress regimes: fly ash was slightly alkaline, saline and heavy-metal-enriched; weathered coal was strongly acidic and saline; and coal gangue was acidic–sulfidic. Dumping significantly altered soil pH, salinity and metal speciation, reducing organic matter by 36–61% and microbial biomass carbon by 45–74%. Alpha diversity responded in a type-specific manner: WC had the lowest Shannon index (4.72, −55.1%) and was dominated by Pseudomonadota (80.6%) with methylotrophy being enriched; FA retained the highest diversity (11.23); and CG was dominated by Actinobacteriota (45.1%). PERMANOVA and ANOSIM confirmed significant community separation. Nitrification was nearly lost under WC, while sulfate respiration was enriched under CG and sulfur respiration under FA, indicating type-specific restructuring of nitrogen and sulfur cycling. These results show that CBSW dumping drives restructuring and nitrogen–sulfur functional degradation of black-soil bacterial communities through type-specific environmental filtering, with weathered coal causing the most severe degradation, informing classified remediation.