Modification of the properties of salt affected soils using electrochemical treatments
- Jayasekera, Samudra, Hall, Stephen
- Authors: Jayasekera, Samudra , Hall, Stephen
- Date: 2007
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 25, no. 1 (2007), p. 1-10
- Full Text:
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- Description: In this project, an in situ soil treatment technique using the principles of electrokinetics was tested using laboratory experimental models in order to identify the potential of this approach in modifying and reinstating the physical properties of salt affected soils. Experiments were conducted in the laboratory using saline-sodic soils collected from two salt affected regions in central Victoria, Australia. Soil specimens were compacted in glass tanks to reproduce in situ density and in situ water content. Using mild steel electrodes inserted into the soil, a direct current was passed through the soil under a constant potential gradient of 0.5 V/cm for a period of 14 days. In separate experiments, distilled water and a saturated lime solution were introduced to the soil via the anode over this experimental period. It was observed that the soil dispersion, otherwise known as soil sodicity (measured as ESP - Exchangeable Sodium Percentage and SAR - Sodium Absorption Ratio) decreased by up to 90% in most regions of the soil between the electrodes. The compressive strength of the soil increased in excess of 100% with electrokinetic treatment alone while the lime-enhanced electrokinetic treatment led to an almost 200% strength increase. The liquid limit and plastic limit of the soil increased causing the plasticity index to decrease, indicating increases in soil compressive strength and workability. These results indicate the potential of this technique for improving the physical properties of salt affected soils both effectively and efficiently, and in particular gives hope for the remediation of salt affected land for infrastructure management and development. © Springer Science+Business Media, Inc. 2006.
- Description: C1
- Description: 2003004772
- Authors: Jayasekera, Samudra , Hall, Stephen
- Date: 2007
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 25, no. 1 (2007), p. 1-10
- Full Text:
- Reviewed:
- Description: In this project, an in situ soil treatment technique using the principles of electrokinetics was tested using laboratory experimental models in order to identify the potential of this approach in modifying and reinstating the physical properties of salt affected soils. Experiments were conducted in the laboratory using saline-sodic soils collected from two salt affected regions in central Victoria, Australia. Soil specimens were compacted in glass tanks to reproduce in situ density and in situ water content. Using mild steel electrodes inserted into the soil, a direct current was passed through the soil under a constant potential gradient of 0.5 V/cm for a period of 14 days. In separate experiments, distilled water and a saturated lime solution were introduced to the soil via the anode over this experimental period. It was observed that the soil dispersion, otherwise known as soil sodicity (measured as ESP - Exchangeable Sodium Percentage and SAR - Sodium Absorption Ratio) decreased by up to 90% in most regions of the soil between the electrodes. The compressive strength of the soil increased in excess of 100% with electrokinetic treatment alone while the lime-enhanced electrokinetic treatment led to an almost 200% strength increase. The liquid limit and plastic limit of the soil increased causing the plasticity index to decrease, indicating increases in soil compressive strength and workability. These results indicate the potential of this technique for improving the physical properties of salt affected soils both effectively and efficiently, and in particular gives hope for the remediation of salt affected land for infrastructure management and development. © Springer Science+Business Media, Inc. 2006.
- Description: C1
- Description: 2003004772
Application of thermal fragmentation in Australian hard rock underground narrow-vein mining
- Drake, Bradley, Koroznikova, Larissa, Tuck, Michael, Durkin, Steve
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
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- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
- Authors: Drake, Bradley , Koroznikova, Larissa , Tuck, Michael , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 1 (2020), p. 219-229
- Full Text:
- Reviewed:
- Description: This paper presents the results from the investigation of the application of thermal fragmentation in Australian hard rock underground narrow-vein mining. Two geologically similar samples from an underground narrow-vein hard rock gold mine were collected to obtain a measure of the technology’s ability to recover ore by the creation of large thermal openings to assess the applicability of the thermal method. Particle size distribution showed a higher generation of fine product, − 2 mm, by thermal fragmentation compared with selective blasting by 31%. The Bond work index for thermal ore (12.62 kWh/t) is half to that of the blasted ore value (25.32 kWh/t). The average grindability obtained for the thermal ore sample was greater than the blasted sample by a factor of 2.44, a higher value indicating a decrease in the energy required to grind. The thermal fragmentation method generates product with higher dissolution of gold in cyanide, by 14% for the − 9.5 + 2 mm size fraction samples. Additionally, the thermal fragmentation results in higher production of − 9.5 + 2 mm material by 15 % compared with selective blasting. © 2019, Society for Mining, Metallurgy & Exploration Inc.
Thermal fragmentation as a possible, viable, alternative mining method in narrow vein mining?
- Bouwmeester, Patrick, Tuck, Michael, Koroznikova, Larissa, Durkin, Steve
- Authors: Bouwmeester, Patrick , Tuck, Michael , Koroznikova, Larissa , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 2 (2020), p. 605-618
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- Description: In currently used mining methods, blasting techniques often causes dilution of valuable ore and results in costly processing requirements. In the context of narrow vein mining of thin and highly concentrated orebodies there is a need of a mining method that can reduce dilution in order to remain economically viable. This research project explored the viability of a new mining technology, thermal fragmentation, in narrow vein mining. Thermal fragmentation technology uses a flame jet to produce extreme heat that spalls the surrounding rock to a strategically located drill hole, as an alternative to traditional blasting. This paper creates a net present value (NPV) model of a mining method using thermal fragmentation, as well as for an existing method used for narrow vein mining; comparisons and evaluations were made regarding the feasibility of the new technology. It was found that while overall costs for thermal fragmentation were relatively high, reductions in wages, haulage and processing costs, as well as increased productivity and ore recovery, meant that the new method would improve the financial performance of any operation. These results identify that there is an opportunity to introduce the thermal fragmentation technology to narrow vein mines within Australia, in order to lower costs and increase profit. © 2019, Society for Mining, Metallurgy & Exploration Inc.
- Authors: Bouwmeester, Patrick , Tuck, Michael , Koroznikova, Larissa , Durkin, Steve
- Date: 2020
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 37, no. 2 (2020), p. 605-618
- Full Text:
- Reviewed:
- Description: In currently used mining methods, blasting techniques often causes dilution of valuable ore and results in costly processing requirements. In the context of narrow vein mining of thin and highly concentrated orebodies there is a need of a mining method that can reduce dilution in order to remain economically viable. This research project explored the viability of a new mining technology, thermal fragmentation, in narrow vein mining. Thermal fragmentation technology uses a flame jet to produce extreme heat that spalls the surrounding rock to a strategically located drill hole, as an alternative to traditional blasting. This paper creates a net present value (NPV) model of a mining method using thermal fragmentation, as well as for an existing method used for narrow vein mining; comparisons and evaluations were made regarding the feasibility of the new technology. It was found that while overall costs for thermal fragmentation were relatively high, reductions in wages, haulage and processing costs, as well as increased productivity and ore recovery, meant that the new method would improve the financial performance of any operation. These results identify that there is an opportunity to introduce the thermal fragmentation technology to narrow vein mines within Australia, in order to lower costs and increase profit. © 2019, Society for Mining, Metallurgy & Exploration Inc.
A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting
- He, Ziguang, Armaghani, Danial, Masoumnezhad, Mojtaba, Khandelwal, Manoj, Zhou, Jian, Murlidhar, Bhatawdekar
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
- Full Text:
- Reviewed:
- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
- Full Text:
- Reviewed:
- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Zhou, Jian, Dai, Yong, Khandelwal, Manoj, Monjezi, Masoud, Yu, Zhi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
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- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
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