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Design and Numerical Simulation of a Deep Learning-Based Model for Wet Metallurgical Parameter Prediction and Optimization
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Chunyan LIU, Jingpu JIA
Hydrometallurgy of China | 2025, 44(2) : 264 - 270
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Hydrometallurgy of China | 2025, 44(2): 264-270
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Design and Numerical Simulation of a Deep Learning-Based Model for Wet Metallurgical Parameter Prediction and Optimization
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Chunyan LIU, Jingpu JIA
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  • Computer Application Department, Shijiazhuang Information Engineering Vocational College, Shijiazhuang 052161, China
Published: 2025-04-28 doi: 10.13355/j.cnki.sfyj.2025.02.016
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Aiming at the issues of low computational efficiency and insufficient intelligence in predicting hydrometallurgical process parameters, a process optimization control model that utilizes 1D-CNN for predicting copper ion concentration and Seq2Seq for predicting mass transfer rate, with the objective of maximizing economic benefits was proposed. The optimization problem is solved using the DDPG algorithm. The results of numerical simulation and empirical study show that the model can predict the parameters of copper extraction process with high accuracy, realize the effective optimization and adjustment of parameters, and promote the improvement of economic benefits.

copper  /  extraction  /  hydrometallurgy  /  DDPG  /  1D-CNN  /  Seq2Seq  /  numerical simulation
Chunyan LIU, Jingpu JIA. Design and Numerical Simulation of a Deep Learning-Based Model for Wet Metallurgical Parameter Prediction and Optimization[J]. Hydrometallurgy of China, 2025 , 44 (2) : 264 -270 . DOI: 10.13355/j.cnki.sfyj.2025.02.016
Year 2025 volume 44 Issue 2
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doi: 10.13355/j.cnki.sfyj.2025.02.016
  • Receive Date:2024-09-10
  • Online Date:2025-07-05
  • Published:2025-04-28
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  • Received:2024-09-10
Affiliations
    Computer Application Department, Shijiazhuang Information Engineering Vocational College, Shijiazhuang 052161, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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