A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning

Wu, Jing; Cheng, Yan-Ming; Liu, Cheng; Lee, Il-Kyoo; Huang, Wenlin

doi:10.1155/2020/1026128

Cited by 8 publications

(7 citation statements)

References 2 publications

(2 reference statements)

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“…e reasons for this situation may be attributed to the fact that the number and spatial distribution of stations in different months are time-varied, but the parameter values of GA (see Figure 3) are invariant in all months. Additionally, the parameter values affect the optimization performance of the GA [46,47]. Consequently, the GA might not search the optimal connection weights and thresholds for some months under these invariant parameter values, leading to the interpolation accuracies of GA-BPANN in the corresponding months that did not reach the expected optimization effect.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, the most optimal individual, which represents the optimal initial weights and thresholds of the BPANN model, is generated. Figure 3 describes the flowchart of BPANN optimized with GA; for the details of population initialization, fitness function, selection operation, crossover operation, and for mutation operation, refer to [46,47].…”

Section: Ga-bpannmentioning

confidence: 99%

“…Aiming to overcome these intrinsic deficiencies, many researchers have attempted to improve BPANN's performance using intelligent optimization algorithms [42][43][44][45]. Genetic algorithms (GA) feature excellent global search ability [46] and are used to optimize the initial connection weights and thresholds of BPANN (hereinafter, GA-BPANN) in order to avoid falling into a local optimum and improve its training speed and modelling ability [42,47]. erefore, GA-BPANN has been applied in many fields of natural and social sciences for complex nonlinear modelling and prediction, showing excellent performance over other BPANN models optimized by different algorithms [48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

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A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

Fang

2021

Advances in Meteorology

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The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

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Section: Discussionmentioning

confidence: 99%

Section: Ga-bpannmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

Fang

2021

Advances in Meteorology

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“…In fact, compared with the theoretical value, the actual amount of produced copper is decreased by impurities, oxidation and dissolution of cathode sediment, and electrode short circuit and leakage loss. In production practice, current efficiency is proposed to evaluate effective utilization of current, and it is the percentage ratio between the actual production of copper and theoretical production of the copper and is expressed by [27] η…”

Section: Copper Electrowinning Processmentioning

confidence: 99%

An Effective Harmonic Suppression Method Based on Linear Active Disturbance Rejection Control for Copper Electrowinning Rectifier

Cui

Liu

Cheng

et al. 2021

Mathematical Problems in Engineering

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Because the harmonics in the production process of copper electrowinning have an important impact on the electrical energy consumption, it is necessary to suppress the harmonics effectively. In this paper, a copper electrowinning rectifier with double inverse star circuit is selected as a study object in which a large number of harmonics mainly including the 5th, 7th, 11th, and 13th harmonics are generated and injected back into the power grid. The total harmonic distortion rate of the power grid is up to 29.19% before filtering. Therefore, a method combining the induction filtering method and the active filtering method is proposed to carry out comprehensive filtering. Simulation results demonstrate that the total harmonic distortion rate of the system decreases to 4.20%, which indicates that the proposed method can track the corresponding changes of harmonics when the load changes in real time and filter them out. In order to ensure and improve the effect of active filter, a current harmonic tracking control method based on linear active disturbance rejection control is proposed. Simulation results show that the total harmonic distortion rate decreases to 3.34%, which is also lower than that of hysteresis control. Compared with the conventional single filtering method, the new filtering method combining induction filtering with active filtering based on linear active disturbance rejection control in the copper electrowinning rectifier has obvious advantages.

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“…Lv et al combined a BP neural network and the Grey model to predict the settlement of foundation [18]. Wu et al [19] mentioned the common problems of the BP neural network, i.e., the inaccuracy of initial weights and thresholds, which affect the accuracy of the algorithm prediction, and used GA to optimize the BP neural network to improve the accuracy in the problem of energy consumption of copper electrowinning by 14.25%. Based on this, Liu et al [20] used the Grey Verhulst model to improve the GA-BP neural network model and stated an accurate model in settlement prediction.…”

Section: Introductionmentioning

confidence: 99%

Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

Lyu

Samali

et al. 2022

Materials

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Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R2) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications.

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A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning

Cited by 8 publications

References 2 publications

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

An Effective Harmonic Suppression Method Based on Linear Active Disturbance Rejection Control for Copper Electrowinning Rectifier

Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

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