Six novel hybrid extreme learning machine–swarm intelligence optimization (ELM–SIO) models for predicting backbreak in open-pit blasting
- Li, Chuanqi, Zhou, Jian, Khandelwal, Manoj, Zhang, Xiliang, Monjezi, Masoud, Qiu, Yingui
- Authors: Li, Chuanqi , Zhou, Jian , Khandelwal, Manoj , Zhang, Xiliang , Monjezi, Masoud , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 31, no. 5 (2022), p. 3017-3039
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- Description: Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. © 2022, The Author(s).
- Authors: Li, Chuanqi , Zhou, Jian , Khandelwal, Manoj , Zhang, Xiliang , Monjezi, Masoud , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 31, no. 5 (2022), p. 3017-3039
- Full Text:
- Reviewed:
- Description: Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. © 2022, The Author(s).
Comparison and application of top and bottom air decks to improve blasting operations
- Monjezi, Monjezi, Amiri, Hamed, Mousavi, Mir, Hamidi, Jafar, Khandelwal, Manoj
- Authors: Monjezi, Monjezi , Amiri, Hamed , Mousavi, Mir , Hamidi, Jafar , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Aims Geosciences Vol. 9, no. 1 (2022), p. 16-33
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- Description: The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences.
- Authors: Monjezi, Monjezi , Amiri, Hamed , Mousavi, Mir , Hamidi, Jafar , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Aims Geosciences Vol. 9, no. 1 (2022), p. 16-33
- Full Text:
- Reviewed:
- Description: The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences.
Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP) : a case study from an iron ore mine
- Kazemi, Mohammad, Nabavi, Zohreh, Khandelwal, Manoj
- Authors: Kazemi, Mohammad , Nabavi, Zohreh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: AIMS Geosciences Vol. 9, no. 2 (2023), p. 357-381
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- Description: Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations.
- Authors: Kazemi, Mohammad , Nabavi, Zohreh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: AIMS Geosciences Vol. 9, no. 2 (2023), p. 357-381
- Full Text:
- Reviewed:
- Description: Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations.
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