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Minimization of blast-induced dust emission using gene-expression programming and grasshopper optimization algorithm: a smart mining solution based on blasting plan optimization
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DOI：10.1007/s10098-022-02327-9.png)
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In surface mining, blast-induced dust can be discharged to the atmosphere and impact the surrounding environment and nearby residential areas, especially if a large volume of rock is blasted under inappropriate meteorological conditions such as high wind speed. Many attempts have been done to predict the blast-induced dust emission distance but the literature of the dust reduction is limited to change stemming materials based on water capsules. This study develops a methodology using gene expression programming and grasshopper optimization algorithm to find an optimal blasting plan with minimum blast-induced dust in a mine close to sensitive ecosystem and residential areas. The best gene expression programming model, which indicates relationship between dependent and independent variables, was first determined based on 100 blasting data collected from the mine. The model with the R-2 of 0.9559 and 0.9145, respectively, for training and validating parts was chosen as the best model. The model, as an objective function, was considered in grasshopper optimization algorithm to find the optimal blasting plan with minimum dust emission level. Compared to the old blasting plans of the mine, the optimal plan resulted in a reduction of 76.82% in the emission distance of the blast-induced under constant meteorological conditions. Sensitivity analysis on the system parameters revealed the high sensitivity of the output to wind speed, air temperature, air humidity, powder factor, and stemming. [GRAPHICS] .
Keyword:
Mine blasting
Dust dispersion
Optimization algorithm
Gene expression programming
Grasshopper optimization algorithm
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3.9
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3.4K
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7.9K
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