Sustainable Optimization of Fuel Consumption and CO2 Emissions in Mining Haul Trucks using Machine Learning: a Case Study in a Gold Mine in La Libertad, Peru | ||
| Journal of Mining and Environment | ||
| مقاله 7، دوره 17، شماره 1، فروردین و اردیبهشت 2026، صفحه 105-126 اصل مقاله (7.85 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.16051.3097 | ||
| نویسندگان | ||
| Marco Antonio Cotrina-Teatino* 1؛ Jairo Jhonatan Marquina-Araujo1؛ Solio Marino Arango-Retamozo1؛ Luis Alex Rios-LLaure1؛ Jose Nestor Mamani-Quispe2؛ Salomon Ortiz-Quintanilla3 | ||
| 1Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru | ||
| 2Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru | ||
| 3Faculty of Engineering, Mining Engineering School, Universidad Nacional Jorge Basadre Grohmann, Tacna, Peru | ||
| چکیده | ||
| This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were normalized using Z-score scaling. Second, a Ridge regression model was trained to predict fuel consumption based on variables such as truck utilization, trips, road gradient, material type, haul distance, and operating hours. Finally, three operational strategies were simulated: Controlled Reduction (CRS), Balanced Efficiency (BES), and Maximum Utilization (MUS), to evaluate environmental, economic, and social impacts. The results indicated that the Ridge model achieved strong predictive performance in estimating fuel consumption (R2 = 0.83; MSE = 38.16). According to the simulated scenarios, environmentally, CRS reduced fuel consumption by 30% and CO2 emissions by 1,481.3 tons; BES achieved 7.99% savings and 394.9 tons less CO2. Economically, CRS saved USD 664,924.6 in fuel costs and BES USD 177,276.3. Socially, the carbon cost decreased by USD 11,406.1 (CRS) and USD 3,041.0 (BES). MUS increased emissions by 864.3 tons and fuel costs by USD 387,966.4. This research proposes a novel integration of machine learning and sustainability analysis applied to haul trucks in open-pit mining material transport. It also offers a replicable, data-driven framework for mining companies to reduce emissions, optimize costs, and align their operations with sustainability goals. | ||
| کلیدواژهها | ||
| Energy efficiency؛ Carbon dioxide؛ Ridge regression؛ Operational sustainability؛ Fuel consumption | ||
| مراجع | ||
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