Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

Abstract

Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

“…In the mine safety item, a study on the drainage control [94][95][96][97] was conducted to predict the groundwater level or the inflow of mines. Dust control studies evaluated and predicted the air quality of mines [98][99][100]. A study predicted the occurrence of an earthquake [101,102] by considering the time data of the increasing seismic activity in coal mines.…”

“…The highest number of used data was 57, which were acquired from an open source (Figure 6). predicted the air quality of mines [98][99][100]. A study predicted the occurrence of an earthquake [101,102] by considering the time data of the increasing seismic activity in coal mines.…”

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

“…2.1.2 | PSO for model parameters PSO is proposed by Kennedy and Eberhart (1995). Among the existing global optimization algorithms, PSO is one of the most effective methods for non-linear and complex multi-dimensional problems, because the performance of PSO has a strong dependence on the choice set by itself (Bui, Lee, Nguyen, Bac, & Moayedi, 2019;El Gmili, Mjahed, El Kari, & Ayad, 2019;Haghdar, 2019;Zha et al, 2019).…”

Air Pollutant Analysis and AQI Prediction Based on GRA and Improved SOA-SVR by Considering COVID-19

“…Wang and Zhang used a differential evolution algorithm (DE) to find optimal SVR parameters [17,18]. The optimization of SVR parameters by particle swarm optimization (PSO) have been studied in literature [19][20][21]. In recent years, with the development of various new intelligent algorithms, there are more choices for the optimization algorithm of SVR model parameters.…”