A new power supply strategy for high power rectifying units in electrolytic copper process

Liu, Hemiao; Zhao, Yulian; Cheng, Yan-Ming; Wu, Jing; Shurafa, Mahmoud Al; Liu, Cheng; Lee, Il-Kyoo

doi:10.1007/s42835-021-00966-4

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Nodes between two adjacent layers are fully connected, and nodes within a layer are not attached. The forward transmission signal is transmitted from the input layer through each layer node to the output layer, and then the error between the output value and the true value is back propagated to each layer node, and the cycle continues to optimize the weights and thresholds until a satisfactory result is obtained [31][32][33]. However, the prediction accuracy of BP neural networks is greatly influenced by factors such as initial weights and thresholds, which are initialized randomly, making it difficult to obtain the best initial weights and thresholds.…”

Section: Prediction With Ga-bp Neural Networkmentioning

confidence: 99%

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Dong,

2023

Energies

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This paper proposes a Litz winding numerical-simulation model considering the transposition effect, and uses the transient-plane-source method to verify the numerical-simulation method. In addition, numerical methods were adopted to further investigate the impact of filling rate and epoxy-resin type, and their combined effects, on thermal conductivity. To facilitate engineering design, the discrete data points were fitted using the least square method to obtain a straightforward and application-friendly polynomial empirical formula. On this basis, the GA-BP neural network was used to analyze the data in order to seek out more accurate prediction results for the entire data set. As a result, compared with the least square method, the error between the prediction result and the target value in the x direction was reduced by 87.04%, and the error in the z direction was reduced by 84.97%.

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Section: Prediction With Ga-bp Neural Networkmentioning

confidence: 99%

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Dong,

2023

Energies

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“…The results show that the mean square error decreased from 66.67 to 0.02. Liu, He Miao et al [15] proposed a power supply optimization strategy based on the improved BP neural network and compared PSO-BP (Particle Swarm Optimization-Backpropagation) neural network with GA-BP neural network. The results indicated the prediction error of the power supply strategy of GAoptimizing BP (GA-BP) neural network is the minimum.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network

et al. 2023

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The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.

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Analysis of the energy matrix decarbonization in the operation of the large mining power distribution systems: The Chilean case

Pessino,

Morán,

Espinoza

et al. 2023

2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Syst

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A new power supply strategy for high power rectifying units in electrolytic copper process

Cited by 4 publications

References 24 publications

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network

Analysis of the energy matrix decarbonization in the operation of the large mining power distribution systems: The Chilean case

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