Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Hosseini, Shahab; Monjezi, Masoud; Bakhtavar, Ezzeddin; Mousavi, Amin

doi:10.1007/s11053-021-09930-5

Cited by 36 publications

(8 citation statements)

References 43 publications

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“…This analysis enables researchers to identify critical parameters contributing significantly to output variability, facilitating the prioritization of resources and efforts toward addressing and optimizing these influential factors. Moreover, the CAM offers a systematic and structured approach for exploring the impact of parameter changes, allowing researchers to assess how sensitive the model is to both small and large fluctuations in input parameters [140,141]. By conducting sensitivity analysis utilizing the CAM, researchers can make informed decisions regarding the model's design, input parameter selection, and overall robustness.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Zhang,

Wang,

et al. 2024

Buildings

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Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges with its intricate cementitious matrix and a vague mix design, where the components and their relative amounts can influence the compressive strength. In response to these challenges, the application of accurate and applicable soft computing techniques becomes imperative for predicting the strength of such a composite cementitious matrix. This research aimed to predict the compressive strength of GePoCo using waste resources through a novel ensemble ML algorithm. The dataset comprised 156 statistical samples, and 15 variables were selected for prediction. The model employed a combination of the RF, GWO algorithm, and XGBoost. A stacking strategy was implemented by developing multiple RF models with different hyperparameters, combining their outcome predictions into a new dataset, and subsequently developing the XGBoost model, termed the RF–XGBoost model. To enhance accuracy and reduce errors, the GWO algorithm optimized the hyperparameters of the RF–XGBoost model, resulting in the RF–GWO–XGBoost model. This proposed model was compared with stand-alone RF and XGBoost models, and a hybrid GWO–XGBoost system. The results demonstrated significant performance improvement using the proposed strategies, particularly with the assistance of the GWO algorithm. The RF–GWO–XGBoost model exhibited better performance and effectiveness, with an RMSE of 1.712 and 3.485, and R2 of 0.983 and 0.981. In contrast, stand-alone models (RF and XGBoost) and the hybrid model of GWO–XGBoost demonstrated lower performance.

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Section: Resultsmentioning

confidence: 99%

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Zhang,

Wang,

et al. 2024

Buildings

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“…However, depending on the climate in and around the blasting area, significant amounts of dust produced by blasting operations can have negative effects on the environment, human health, and various plant and Unfortunately, it is difficult to lessen the negative effects of the dust that is released during blasting. Consequently, minimizing dust emissions should be prioritized as one of the objectives in optimizing blast design (Hosseini et al 2021).…”

Section: Dust Induced By Blastingmentioning

confidence: 99%

“…In the case of surface mining, the discharge of dust resulting from blasting activities can have adverse effects on the environment and nearby residential areas. These effects are particularly concerning when a substantial amount of rock is blasted under unfavorable meteorological conditions, such as high wind speeds (Hosseini et al 2021). Such environmentally damaging mining practices are contrary to the principles of climate-smart and green mining, and they can severely impact the sustainability of mining towns.…”

Section: Prediction Of Dustmentioning

confidence: 99%

State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Zhou,

Zhang,

Qiu

2024

Artif Intell Rev

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The technological difficulties related with blasting operations have become increasingly significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the threats posed by environmental effect of blasting (EEB). To address these challenges, numerous scholars have conducted extensive research employing various assessment techniques with the aim of mitigating risks and preventing the emergence of unfavorable EEB. The occurrence of EEB is prevalent during the excavation of hard rock, and it presents significant hazards to personnel safety, equipment integrity, and operational continuity. Therefore, conducting a systematic review of EEB is of utmost importance as it enables a comprehensive understanding of the contributing factors. Such an understanding plays a vital role in advancing EEB prediction and prevention methods. The careful selection of an appropriate EEB assessment method is a crucial aspect of blasting operations. However, there is a lack of comprehensive discussions on the applications of machine learning (ML) and optimization algorithms (OA) in addressing various EEB. Only a limited number of papers have briefly touched upon this topic. Therefore, the primary objective of this paper is to bridge this gap by conducting an analysis of global trends using Cit-eSpace and VOSviewer software from the year 2000 onwards. It comprehensively explores EEB classification and definition, encompassing air overpressure (AOp), ground vibration, dust, backbreak, flyrock, and rock fragmentation. Furthermore, the paper provides a compendium of the most recent ML and OA prediction techniques used to addresses EEB. Finally, the paper concludes by proposing future directions for exploring innovative approaches that combine data-driven ML techniques with knowledge-based or physicsbased methods. Such integration has the potential to mitigate hazards during blasting operations and reduce the likelihood of unfavorable EEB occurrences.

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“…Finally, the new data generated in the hidden layer are transferred to the output layer based on a similarly expressed process. It is noteworthy that the important components in the ANNs are the neurons' number in layers because the neurons affect the network performances [47]. Based on this, the number of input and output neurons is equal to the number of imported input and outputs to the system, respectively.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

et al. 2023

Self Cite

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One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.

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Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Cited by 36 publications

References 43 publications

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

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