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为了精确模拟金浸出率的变化过程，研究设计了一个多级浸出模型，并基于前馈神经网络(Forward Neural Network, FNN)和径向基函数(Radial Basis Function, RBF)构建了反应速率预测模型。通过数值仿真与对比试验对模型的有效性进行了验证。结果表明：该模型对金浸出率的预测值与实际值误差保持在2.1%～2.6%之间，适应性较强，精确度较高。
Abstract：In order to accurately simulate the variation process of gold leaching rate, a multistage leaching model was designed, and the reaction rate prediction model based on the Forward Neural Network(FNN) and Radial Basis Function(RBF) was constructed.The validity of the model was verified by numerical simulation and comparative test.The results show that the error between the predicted value and the actual value of the gold leaching rate is between 2.1% and 2.6%,which is effective and accurate.
[1] 郭计敏.金氰化浸出过程非线性预测控制方法及仿真算法分析研究[J].湿法冶金，2023,42(6):659-665.GUO Jimin.Nonlinear predictive control method and simulation algorithm analysis of gold cyanide leaching process[J].Hydrometallurgy of China,2023,42(6):659-665.
[2] 雷江龙，余娟，向明旭，等.基于深度神经网络的数据驱动潮流计算异常误差改进策略[J].电力系统自动化，2022,46(1):76-84.LEI Jianglong,YU Juan,XIANG Mingxu,et al.Improvement strategy for abnormal error of data-driven power flow calculation based on deep neural network[J].Automation of Electric Power Systems,2022,46(1):76-84.
[3] 高军，刘亚东.基于定性定量混合建模的金湿法冶金过程优化研究[J].长春师范大学学报，2022,41(10):65-73.GAO Jun,LIU Yadong.Research on process optimization based on qualitative and quantitative hybrid modeling for the gold hydrometallurgy[J].Journal of Changchun Normal University,2022,41(10):65-73.
[4] 马治卿，高东坡，马磊.湿法冶金硫化镍加压浸出过程的建模方法[J].有色冶金设计与研究，2022,43(4):14-18.MA Zhiqing,GAO Dongpo,MA Lei.Modeling method of nickel sulfide pressure leaching in hydrometallurgical process[J].Nonferrous Metals Engineering & Research,2022,43(4):14-18.
[5] 莫文水.金氰化浸出过程混合建模及仿真算法分析研究[J].湿法冶金，2023,42(4):429-435.MO Wenshui.Hybrid modeling and simulation algorithm of gold cyanide leaching process[J].Hydrometallurgy of China,2023,42(4):429-435.
[6] 朱文刚，盛春岩，范苏丹，等.基于前馈神经网络的多模式集成降水预报研究[J].干旱气象，2024,42(1):117-128.ZHU Wengang,SHENG Chunyan,FAN Sudan,et al.Research on multi-model integrated precipitation forecast based on feed forward neural network[J].Journal of Arid Meteorology,2024,42(1):117-128.
[7] 张利成，鲍煦，李静，等.湍流扩散环境中基于前馈神经网络的信源定位算法[J].东南大学学报(自然科学版),2023,53(2):370-376.ZHANG Licheng,BAO Xu,LI Jing,et al.Source localization algorithm based on feed-forward neural network in turbulent diffusion environment[J].Journal of Southeast University:Natural Science Edition,2023,53(2):370-376.
[8] 王昭昳，张涛，杨滨，等.基于径向基函数神经网络的脑损伤电阻抗成像仿真研究[J].中国医学装备，2023,20(3):1-5.WANG Zhaoxi,ZHANG Tao,YANG Bin,et al.Simulation study of electrical impedance imaging of brain injury based on RBF neural network[J].China Medical Equipment,2023,20(3):1-5.
[9] 刘泓杉，刘慧博.基于径向基函数神经网络的永磁同步电机转速自适应控制策略[J].电子器件，2023,46(6):1552-1560.LIU Hongsan,LIU Huibo.Speed adaptive control strategy of permanent magnet synchronous motor based on RBF neural network[J].Chinese Journal of Electron Devices,2023,46(6):1552-1560.
[10] 姚锐，李俊，惠萌，等.基于集成学习的自适应提升分类模型的局部放电识别研究[J].电网技术，2022(6):2410-2419.YAO Rui,LI Jun,HUI Meng,et al.Pattern recognition for partial discharge using adaptive boost classification model based on ensemble method[J].Power System Technology,2022(6):2410-2419.
[11] 冯志友，张燕，杨廷力，等.基于牛顿欧拉法的2UPS-2RPS并联机构逆动力学分析[J].农业机械学报，2009(4):193-197.FENG Zhiyou,ZHANG Yan,YANG Tingli,et al.Inverse dynamics of a 2UPS-2RPS parallel mechanism by newton-euler formulation[J].Transactions of the Chinese Society for Agricultural Machinery,2009(4):193-197.
[12] 周新宇，尹子悦，高卫峰，等.一种基于强化学习的自适应多邻域人工蜂群算法[J].计算机学报，2024,47(7):1521-1546.ZHOU Xinyu,YIN Ziyue,GAO Weifeng,et al.Adaptive multi-neighborhood artificial bee colony algorithm based on reinforcement learning[J].Chinese Journal of Computers,2024,47(7):1521-1546.
基本信息:
DOI：10.13355/j.cnki.sfyj.2025.03.017
中图分类号:TF831
引用信息:
[1]曹红,李庆华.神经网络建模的金浸出过程及其数值仿真研究[J].湿法冶金,2025,44(03):424-431.DOI:10.13355/j.cnki.sfyj.2025.03.017.
基金信息:
浙江省教育厅支撑计划项目(Y202249939)