Experts are increasingly interested in incorporating environmental and climate action into all projects. Mining projects face a broader range of environmental concerns. Since most mines nowadays rely on blasting operations, a considerable volume of dust is released into the atmosphere. The environmentally destructive mining practices contradict the principles of green and climate-smart mining and substantially impact the sustainability of mining communities. Estimating emissions and assessing risks associated with mine dust are critical components of blast design in mining projects. The main research objective is to model the monitored blast-induced dust emission in surface mines. In mining and civil projects, the rock engineering system (RES) is a common method for conducting risk analyses by considering causal–effect relationships between involve parameters in the system. However, conventional RES deals with uncertain and imprecise information. Therefore, the current study incorporates the Z-number theory with the conventional RES for presenting a reliability-based rock engineering system (RRES) to address erroneous and defective information resulting in uncertainty and to increment reliability of decision-makers. In fact, the novelty of this paper is integration of Z-number concept with RES. This approach is developed to evaluate the cause–effect relationship between effective parameters and distribution of blast-induced dust emission (DBID). Dealing with incomplete information is one of the most obvious main challenges of this research. The proposed approach eliminated the uncertainty of expert-based systems and increased the accuracy of modeling results, which is one of the main advantages of Z-number. Moreover, a sensitivity analysis is carried out using the cosine amplitude method (CAM) to find the most influential parameter on the DBID. Based on obtained results, the vulnerability index (VI) was 49.08 regarding to recorded dust emission data in the Asgaeabad2 limestone mine located in Iran, which this value indicated that the ecological risk relevant to dust dispersion is medium to high. Results reveal that hole diameter (R), specific gravity (SG), and porosity (P) have high weights among the influential parameters. The sensitivity analysis indicates that the powder factor is the most influential parameter of DBID. The performance and accuracy level of the developed RRES model were compared with the statistical models using evaluation indicators, involving variance accounted for (VAF), coefficient of determination (R2), mean absolute relative error (MARE), and balance relative error (BIAS). Obtaining the R2, MARE, VAF and BIAS values of (0.942, 0.022, 94.159, 1.086) and (0.751, 0.044, 75.075, 2.135) for the RRES and statistical model, respectively, showed the success of RRES in predicting and assessing risk value of dust emission due to bench blasting. Therefore, it can be concluded that the proposed RRES is a reliable technique for both risk assessment and dust emission prediction.
کلید واژگان :Dust emission · Rock engineering system · Clean blasting · Green and climate-smart mining
ارزش ریالی : 500000 ریال
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