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Faizan Arshad
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Enhancing sustainability in mining by reducing hauling energy consumption through optimization of distance and slope with semi-mobile in-pit crushers and conveyors
10.1038/s41598-025-06534-4 · 2025
Application of machine learning techniques to predict haul truck fuel consumption in open-pit mines
2022
Modeling productivity reduction and fuel consumption in open-pit mining trucks by considering the temporary deterioration of mining roads through discrete-event simulation
10.3390/mining3010006 · 2023
Prediction of fuel consumption of mining dump trucks: a neural networks approach
10.1016/j.apenergy.2015.04.064 · 2015
A comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation
10.1016/j.ijmst.2016.09.006 · 2016
Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption
10.1016/j.ijmst.2015.12.015 · 2016
Energy consumption in open-pit mining operations relying on reduced energy consumption for haulage using in-pit crusher systems
10.1016/j.jclepro.2020.125228 · 2021
Environmental and economic comparison of diesel and electric trucks in open-pit mining operations
10.1016/j.jclepro.2025.145540 · 2025
The influence of the mining operation environment on the energy consumption and technical availability of truck haulage operations in surface mines
10.3390/en17112654 · 2024
Transforming mining energy optimization: a review of machine learning techniques and challenges
10.3389/fenrg.2025.1569716 · 2025
Equipment selection for surface mining: a review
10.1287/inte.2013.0732 · 2014
A review of thermal energy storage technologies for seasonal loops
10.1016/j.energy.2021.122207 · 2022
A review of operations research in mine planning
10.1287/inte.1090.0492 · 2010
Optimizing multi-team system behaviors: insights from modeling team communication
10.1016/j.ejor.2016.08.036 · 2017
Does the financialization of natural resources lead toward sustainability? An application of advance panel Granger non-causality
10.1016/j.resourpol.2022.102989 · 2022
Real-time truck dispatching in open-pit mines
10.1080/17480930.2023.2201120 · 2023
Gasification behavior of coal and woody biomass: validation and parametrical study
10.1016/j.apenergy.2016.05.119 · 2017
Open-pit mine truck fuel consumption pattern and application based on multi-dimensional features and XGBoost
2021
Haul truck fuel consumption modeling under random operating conditions: a case study
2022
Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM
10.1016/j.energy.2024.131221 · 2024
Simulation-based optimization approach for material dispatching in continuous mining systems
10.1016/j.ejor.2018.12.015 · 2019
Simulation-based optimization: implications of complex adaptive systems and deep uncertainty
10.3390/info13100469 · 2022
Harris hawks optimization: a comprehensive review of recent variants and applications
10.1007/s00521-021-05720-5 · 2021
Understanding loading practices in trucks and shovels in open-pit mining under operation uncertainties
2022
Reinforcement learning-based fleet dispatching for greenhouse gas emission reduction in open-pit mining operations
10.1016/j.resconrec.2022.106664 · 2023
Systematic review on prediction of haulage truck fuel consumption in open-pit mines
10.1080/17480930.2024.2357509 · 2024
Evolutionary design of neural network architectures: a review of three decades of research
10.1007/s10462-021-10049-5 · 2022
Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation
10.1016/j.cscee.2023.100351 · 2023
Low cost GNSS trimble BD982 and u-blox performance test analysis F9 series for several measurement methods (case study: sidoarjo regency)
2022
An optimization model for the real-time truck dispatching problem in open-pit mining operations
10.1007/s11081-022-09780-x · 2023
Resource, economic, and carbon benefits of end-of-life trucks' urban mining in China
10.1038/s43247-025-02832-x · 2025
Predicting fuel consumption and emissions using GPS-based machine learning models for gasoline and diesel vehicles
10.3390/su17062395 · 2025
Global carbon dioxide removal potential of waste materials from metal and diamond mining
10.3389/fclim.2021.694175 · 2021
Unresolved referenced work
2023
Unresolved referenced work
2023
Evaluation of the impacts of the increased axle load on a high axle load freight railway system
2024
Process optimization in sea ports: integrating sustainability and efficiency through a novel mathematical model
10.3390/jmse13010119 · 2025
Powered haulage safety, challenges, analysis, and solutions in the mining industry; a comprehensive review
10.1016/j.rineng.2023.101684 · 2024
Powered haulage safety, challenges, analysis, and solutions in the mining industry; a comprehensive review
10.1016/j.rineng.2023.101684 · doi-reference
Process optimization in sea ports: integrating sustainability and efficiency through a novel mathematical model
10.3390/jmse13010119 · doi-reference
Global carbon dioxide removal potential of waste materials from metal and diamond mining
10.3389/fclim.2021.694175 · doi-reference
Predicting fuel consumption and emissions using GPS-based machine learning models for gasoline and diesel vehicles
10.3390/su17062395 · doi-reference
Resource, economic, and carbon benefits of end-of-life trucks' urban mining in China
10.1038/s43247-025-02832-x · doi-reference
An optimization model for the real-time truck dispatching problem in open-pit mining operations
10.1007/s11081-022-09780-x · doi-reference
Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation
10.1016/j.cscee.2023.100351 · doi-reference
Evolutionary design of neural network architectures: a review of three decades of research
10.1007/s10462-021-10049-5 · doi-reference
Systematic review on prediction of haulage truck fuel consumption in open-pit mines
10.1080/17480930.2024.2357509 · doi-reference
Reinforcement learning-based fleet dispatching for greenhouse gas emission reduction in open-pit mining operations
10.1016/j.resconrec.2022.106664 · doi-reference
Harris hawks optimization: a comprehensive review of recent variants and applications
10.1007/s00521-021-05720-5 · doi-reference
Simulation-based optimization: implications of complex adaptive systems and deep uncertainty
10.3390/info13100469 · doi-reference
Simulation-based optimization approach for material dispatching in continuous mining systems
10.1016/j.ejor.2018.12.015 · doi-reference
Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM
10.1016/j.energy.2024.131221 · doi-reference
Gasification behavior of coal and woody biomass: validation and parametrical study
10.1016/j.apenergy.2016.05.119 · doi-reference
Real-time truck dispatching in open-pit mines
10.1080/17480930.2023.2201120 · doi-reference
Does the financialization of natural resources lead toward sustainability? An application of advance panel Granger non-causality
10.1016/j.resourpol.2022.102989 · doi-reference
Optimizing multi-team system behaviors: insights from modeling team communication
10.1016/j.ejor.2016.08.036 · doi-reference
A review of operations research in mine planning
10.1287/inte.1090.0492 · doi-reference
A review of thermal energy storage technologies for seasonal loops
10.1016/j.energy.2021.122207 · doi-reference
Equipment selection for surface mining: a review
10.1287/inte.2013.0732 · doi-reference
Transforming mining energy optimization: a review of machine learning techniques and challenges
10.3389/fenrg.2025.1569716 · doi-reference
The influence of the mining operation environment on the energy consumption and technical availability of truck haulage operations in surface mines
10.3390/en17112654 · doi-reference
Environmental and economic comparison of diesel and electric trucks in open-pit mining operations
10.1016/j.jclepro.2025.145540 · doi-reference
Energy consumption in open-pit mining operations relying on reduced energy consumption for haulage using in-pit crusher systems
10.1016/j.jclepro.2020.125228 · doi-reference
Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption
10.1016/j.ijmst.2015.12.015 · doi-reference
A comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation
10.1016/j.ijmst.2016.09.006 · doi-reference
Prediction of fuel consumption of mining dump trucks: a neural networks approach
10.1016/j.apenergy.2015.04.064 · doi-reference
Modeling productivity reduction and fuel consumption in open-pit mining trucks by considering the temporary deterioration of mining roads through discrete-event simulation
10.3390/mining3010006 · doi-reference
Enhancing sustainability in mining by reducing hauling energy consumption through optimization of distance and slope with semi-mobile in-pit crushers and conveyors
10.1038/s41598-025-06534-4 · doi-reference