Journal of Energy & Environmental Sciences https://www.journals.cincader.org/index.php/eesj <p><em>Journal of Energy &amp; Environmental Sciences</em> is a peer-reviewed, international and interdisciplinary research journal that focuses on all aspects of energy and environmental sciences. <em>Journal of Energy &amp; Environmental Sciences</em> publishes issues twice a year since 2017. </p> <p>All articles published are Open Access for readers and an article processing charge (APC) applies to papers accepted after peer review.</p> CINCADER Publishing en-US Journal of Energy & Environmental Sciences 2523-0905 Prediction of energy consumption in grinding using artificial neural networks to improve the distribution of fragmentation size [Predicción del consumo de energía en la molienda utilizando redes neuronales artificiales para mejorar la distribución del tamaño de la fragmentación] https://www.journals.cincader.org/index.php/eesj/article/view/206 <p>The study focuses on the prediction of energy consumption in grinding processes using artificial neural networks (ANN). The purpose was to develop a predictive model based on artificial neural networks to estimate energy consumption in grinding and improve the fragmentation size distribution, which is crucial for the efficiency of mining and metallurgical operations. Energy consumption in grinding represents a significant part of operating costs and directly influences the profitability of operations. The ANN was trained from a data set of 126 records, which were divided into 80% for training and 20 % for model testing. The results of this research highlight optimal performance of the predictive model with performance metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficient (R2), with values of 0.78, 1.39, 1.18 and 0.98, respectively in the estimation of energy consumption in the grinding process. Finally, these results indicate that the ANN achieved an accurate prediction of energy consumption in the grinding process, this will allow better baking in energy optimization.</p> Jaime Yoni Anticona Cueva Jhon Vera Encarnación Tomas Jubencio Anticona Cueva Juan Antonio Vega Gonzáles Copyright (c) 2024 Journal of Energy & Environmental Sciences https://creativecommons.org/licenses/by/4.0 2024-03-10 2024-03-10 8 1 1 13 10.32829/eesj.v8i1.206 Multivariable predictive models for the estimation of power consumption (kW) of a Semi-autogenous mill applying Machine Learning algorithms [Modelos predictivos multivariables para la estimación de consumo de potencia (kW) de un molino Semi - autógeno aplicando algoritmos de Machine Learning] https://www.journals.cincader.org/index.php/eesj/article/view/207 <p>This research aimed to develop machine learning (ML) models to estimate power consumption (Kw) in a Semi-autogenous mill in the mining industry. Using Machine Learning algorithms considering various operating variables for the different models such as Multiple Linear Regression (RLM), Decision Tree Regression (RAD), Random Forest Regression (RBA) and Regression Artificial Neural Networks (ANN). The methodology adopted was applied, with an experimental design with a descriptive and transversal approach. The results of the application of these models revealed significant differences in terms of predictive efficiency. The RLM and RRNA stood out with coefficients of determination (R²) of 0.922 and 0.939, respectively, indicating a substantial capacity to explain the variability in power consumption. In contrast, the tree-based models (RAD and RBA) showed inferior performance, with R² of 0.762 and 0.471. When analyzing key metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Root Mean Square Error (RMSE), it was confirmed that both RLM and RRNA outperformed the tree-based models. These results support the choice of RLM and RRNA as preferred models for estimating power consumption in a Semi-autogenous mill.</p> Miguel Angel Vera Ruiz Juan Antonio Vega Gonzales Franklin Jhoan Bailon Villalba Copyright (c) 2024 Journal of Energy & Environmental Sciences https://creativecommons.org/licenses/by/4.0 2024-03-10 2024-03-10 8 1 14 31 10.32829/eesj.v8i1.207 Determination of energy consumption according to the phases of the mineral comminution process [Determinación del consumo energético según las fases del proceso de conminución de minerales] https://www.journals.cincader.org/index.php/eesj/article/view/209 <p>The objective of the research was to study the equations used in energy expenditure for the mineral comminution area and its importance. The focus of the article was theoretical, in which bibliographic information from different convincing and reliable sources of information was grouped, which were analyzed and summarized in graphs and tables to better understand the information; Likewise, the importance that these have in the mineral commercialization process and the production phases where the equations are used was made known; For a better understanding, it is necessary to know about the energy used by the elements where the crushing and grinding stages occur. The equations studied to find this energy are four; the specific energy equation, Rittinger's postulate, Kick's postulate and Bond's postulate, all explained with their characteristics and the data they consider to be resolved. Subsequently, a comparison of their main differences was presented in addition to a practical example where The three equations and the procedure to solve them were applied. We conclude that the importance of the Rittinger, Kick and Bond equations lies in the need for them to calculate costs and to begin the commercialization of minerals, the energy consumed above all will depend on the characteristics of the rock, such as size, type of valuable mineral present and the stage of comminution in which they are found.</p> Jackelin Sofia Diaz Alvarez Iris Ysela Lopez Jara Copyright (c) 2024 Journal of Energy & Environmental Sciences https://creativecommons.org/licenses/by/4.0 2024-03-15 2024-03-15 8 1 32 43 10.32829/eesj.v8i1.209