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A big data-driven hydrometallurgical full-process optimization control model was proposed. Firstly, a LSTM model based on improved attention mechanism was constructed, and the self-attention mechanism was used to enhance the model’s attention to key time steps to improve the prediction accuracy. Secondly, the dynamic adjustment and control of real-time production parameters are realized by using DDQN algorithm improved by priority experience playback. Finally, the optimal control model of economic benefit of the whole process is designed to obtain the optimal solution. The results show that the coefficient of determination R² of the model is 0.92, 0.90, 0.92 in different numerical simulations, which shows that the model is superior to the traditional method in many aspects.

, authors=Yunlong CAI, authorsList=Yunlong CAI, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308544000668295187, articleId=1308544000148201489, tenantId=1146029695717560320, journalId=1146120122248306696, language=CN, title=大数据驱动的湿法冶金全流程优化控制模型及实证研究, columnId=1152626641328501305, journalTitle=湿法冶金, columnName=试验研究, runingTitle=null, highlight=null, articleAbstract=

提出了一个大数据驱动的湿法冶金全流程优化控制模型，首先构建基于注意力机制改进的LSTM模型，通过自注意力机制增强模型对关键时间步的关注提高预测精度；其次，采用优先经验回放改进的DDQN算法实现实时生产参数的动态调整与控制；最后，设计全流程经济效益最优化控制模型获得最优解。结果表明：所提出模型在不同的数值仿真中的决定系数R²达0.92，0.90，0.92，表明该模型在多个方面优于传统方法。

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蔡云龙（1981—），男，硕士，副教授，主要研究方向为计算机、大数据挖掘。

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蔡云龙（1981—），男，硕士，副教授，主要研究方向为计算机、大数据挖掘。

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