Journal of Energy & Environmental Sciences最新文献

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] 根据矿物粉碎过程的不同阶段确定能源消耗 [根据矿物粉碎过程的不同阶段确定能源消耗]。

Journal of Energy & Environmental Sciences Pub Date : 2024-03-15 DOI: 10.32829/eesj.v8i1.209

Jackelin Sofia Diaz Alvarez, Iris Ysela Lopez Jara

{"title":"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]","authors":"Jackelin Sofia Diaz Alvarez, Iris Ysela Lopez Jara","doi":"10.32829/eesj.v8i1.209","DOIUrl":"https://doi.org/10.32829/eesj.v8i1.209","url":null,"abstract":"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.","PeriodicalId":194195,"journal":{"name":"Journal of Energy & Environmental Sciences","volume":"67 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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] 应用机器学习算法估算半自磨机耗电量（千瓦）的多变量预测模型 [Modelos predictivos multivariables para la estimación de consumo de potencia (kW) de un molino Semi - autógeno aplicando algoritmos de Machine Learning] （半自磨机耗电量（千瓦）多变量预测模型应用机器学习算法

Journal of Energy & Environmental Sciences Pub Date : 2024-03-10 DOI: 10.32829/eesj.v8i1.207

Miguel Angel Vera Ruiz, Juan Antonio Vega Gonzáles, Franklin Jhoan Bailon Villalba

{"title":"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]","authors":"Miguel Angel Vera Ruiz, Juan Antonio Vega Gonzáles, Franklin Jhoan Bailon Villalba","doi":"10.32829/eesj.v8i1.207","DOIUrl":"https://doi.org/10.32829/eesj.v8i1.207","url":null,"abstract":"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.","PeriodicalId":194195,"journal":{"name":"Journal of Energy & Environmental Sciences","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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] 利用人工神经网络预测碾磨能耗，改善破碎粒度分布 [利用人工神经网络预测碾磨能耗，改善破碎粒度分布]。

Journal of Energy & Environmental Sciences Pub Date : 2024-03-10 DOI: 10.32829/eesj.v8i1.206

Jaime Yoni Anticona Cueva, Jhon Kener Vera Encarnación, Tomas Jubencio Anticona Cueva, Juan Antonio Vega Gonzáles

{"title":"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]","authors":"Jaime Yoni Anticona Cueva, Jhon Kener Vera Encarnación, Tomas Jubencio Anticona Cueva, Juan Antonio Vega Gonzáles","doi":"10.32829/eesj.v8i1.206","DOIUrl":"https://doi.org/10.32829/eesj.v8i1.206","url":null,"abstract":"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.","PeriodicalId":194195,"journal":{"name":"Journal of Energy & Environmental Sciences","volume":"75 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proposal on the use of compost based on market waste: a sustainable alternative [Propuesta sobre el uso de compost a base de residuos de mercados: una alternativa sostenible] 关于以市场废物为基础使用堆肥的建议:一种可持续的替代办法[以市场废物为基础使用堆肥的建议:一种可持续的替代办法]

Journal of Energy & Environmental Sciences Pub Date : 2022-10-10 DOI: 10.32829/eesj.v6i2.176

Miguel Angel Inga Sotelo, Ana Cecilia De Paz Lazaro, Reynaldo Francisco Cherrepano Manrique, Víctor Henrry Morales Pacora, Manuel Nicolás Morales Alberto, Mario Humberto Taipe Cancho, Jorge Wilson Leiva Gonzales, C. B. Brito Mallqui, Yocet Yojan Rosales Depaz, Cirilo Mario Ccaira Mamani

{"title":"Proposal on the use of compost based on market waste: a sustainable alternative [Propuesta sobre el uso de compost a base de residuos de mercados: una alternativa sostenible]","authors":"Miguel Angel Inga Sotelo, Ana Cecilia De Paz Lazaro, Reynaldo Francisco Cherrepano Manrique, Víctor Henrry Morales Pacora, Manuel Nicolás Morales Alberto, Mario Humberto Taipe Cancho, Jorge Wilson Leiva Gonzales, C. B. Brito Mallqui, Yocet Yojan Rosales Depaz, Cirilo Mario Ccaira Mamani","doi":"10.32829/eesj.v6i2.176","DOIUrl":"https://doi.org/10.32829/eesj.v6i2.176","url":null,"abstract":"The rise in food, decrease in jobs have affected economic progress. For this reason, research was carried out on the use of market waste for the benefit of the community. The objective was to determine the quantification and chemical characterization for the benefit of the community. It is based on descriptive methodology; Since the waste generated in the Miracle of Barranca market was characterized and classified, and a survey was prepared, what is your proposal on the use of compost based on market waste? Once the data was obtained, they were processed using basic statistics. It was determined that per day it produces organic waste with 65.00%, inorganic with 28.67% and others with 6.33%, and per month organic with 68.13%, inorganic with 26.63% and others with 5.24% of the total of 10,341 tons/month of August and chemical composition. of the compost has adequate concentration of organic matter, low nitrogen, phosphorus, potassium, magnesium and high pH 8.54. In the proposal for the use of compost, 40% stands out for agricultural use. It is concluded that through the catheterization of the waste, 65% organic is produced and when making the compost it has a concentration of nutrients that are favorable to improve the properties of the soil and strengthen the plant. Therefore, the use of compost is a sustainable alternative for agriculture.","PeriodicalId":194195,"journal":{"name":"Journal of Energy & Environmental Sciences","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129114710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Journal of Energy &amp; Environmental Sciences最新文献

Journal of Energy & Environmental Sciences最新文献