Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations

Awuah–Offei, Kwame

doi:10.1016/j.jclepro.2016.01.035

Cited by 58 publications

(20 citation statements)

References 48 publications

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“…The energy productivity was measured in this study as the ratio of total materials conveyed to conveyor energy consumed. In mine hauling operations, the amount of product (e.g., the tonnage of material) is often used as a proxy for useful work performed, and the energy consumed in haulage is often used as a measure of energy input [44]. The energy productivity reflects the effectiveness of earth-moving operations.…”

Section: Data Setmentioning

confidence: 99%

Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling

Tsolas

2021

Mining

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This paper proposes a stochastic frontier model for measuring both technical and environmental performance at the mine level by using a translog production function. The Kardia Field opencast lignite mine of the Greek Public Power Corporation (PPC), S.A. is the topic of the case study. Efficiency ratings are derived over a long period of time using annual operating data, and in addition, the determinants of inefficiency are established by means of the technical inefficiency effects model. In the light of the results, there is a strong correlation between technical and environmental efficiency; the results are validated by those produced by data envelopment analysis (DEA). In addition, the stripping ratio is identified as the statistically significant determinant of performance. The proposed framework could be used as an instrument to measure the efficiency of lignite mining operations and to identify the drivers of performance.

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Section: Data Setmentioning

confidence: 99%

Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling

Tsolas

2021

Mining

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“…Figure 2b presents the process of moving through the input layer and hidden layers to produce the target values at the output layer. Equations (1) and (2) present the formula that uses the input variables to calculate the target value, where and are the input variables, and are the hidden layer node values, is the activation function, is the target value, is the weight, and is the bias. The activation function uses the weighted sum of nodes as the input and converts it into a valid value.…”

Section: Principle Of Dnnmentioning

confidence: 99%

“…The design and operation of haulage systems are very important in mining sites. This is because mining costs may vary significantly according to the design and operation methods [1]. Various simulation techniques have been used for haulage systems in mines [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

Baek

Choi

2019

Applied Sciences

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A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

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“…Performance of jumbo operators is a very crucial parameter within mining productivity. This performance is made considering several environmental and field aspects which impact directly in operator's results; hence, operators' performance in conjunction with operating conditions, mine planning and design impact energy efficiency of equipment which is consequently transformed in future productivities and final profits (Awuah-Offei, 2016). The aspects that influence the most in the performance of jumbo operators are geological and geotechnical characteristics of the labor, equipment constraints and daily variables in mining operations (Kahraman, 2006).…”

Section: Introductionmentioning

confidence: 99%

Underground Mine Drilling Predictions using Artificial Neural Networks for Short Term Mine Planning

2020

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Performance of jumbo operators directly affects productivity in current mining opera-tions. Implementing tools to attack low productivity sources is the main focus of the mining industry due to the transition from “easy” deposits to remote, low grade and deeper deposits. Moreover, im-proving equipment productivities strongly impact by producing higher values of outputs with the same values of inputs. This paper presents a method to predict the performance of jumbo operators, seeking to link this predicted data into an efficient short mine planning. During 2018 several data related to jumbo operations was collected in order to create a manual control room to analyze real downtimes and their main sources. After data collection, typing, filtering, and adaptation were performed in order to prepare the database to train the Artificial Neural Network (ANN). Once the ANN was trained, an R2 of 0.9998 was obtained between the real value and the predicted value, finding that the proposed methodology is of great help when carrying out the mining planning in the short term within the mining operation.

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Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations

Cited by 58 publications

References 48 publications

Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling

Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

Underground Mine Drilling Predictions using Artificial Neural Networks for Short Term Mine Planning

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