控制理论与应用2024，Vol.41Issue(3)：454-467,14.DOI:10.7641/CTA.2023.20871
基于多任务学习多目标优化的稀土元素组分含量与浓度多维度软测量
Content and concentration of rare earth element components based on multi-task learning multi-objective optimization multidimensional soft measurement
摘要
Abstract
Online soft measurement of the component content of each element in a mixed rare earth extraction solution is a prerequisite for optimizing the continuous extraction production process and ensuring high purity of the product.Existing soft measurement methods can solve for individual rare earth element fractions independently,but ignore the commonality between multi-element fractions or between fractions and other relevant factors(e.g.concentration).A multi-task learning approach is used to explore the commonality between the component content of multiple rare earth elements and between the component content and concentration in soft measurements of rare earth elements.Firstly,a multi-task deep neural network is constructed to improve the generalization ability and robustness of the model.Secondly,a multi-objective optimization algorithm is proposed to improve the prediction accuracy of each task by searching the Pareto optimum.After several sets of comparison experimental results,it is shown that the method has the best performance when the multi-element component content or multi-element component content and concentration are trained at the same time,which can meet the accuracy and real-time performance of online detection of rare earth elemental component content.关键词
稀土萃取/组分含量/多任务学习/多目标优化/机器学习/深度学习/帕累托Key words
rare earth extraction/component content/multi-task learning/multi-objective optimization/machine learn-ing/deep learning/Pareto引用本文复制引用
张水平,张奇涵,王碧..基于多任务学习多目标优化的稀土元素组分含量与浓度多维度软测量[J].控制理论与应用,2024,41(3):454-467,14.基金项目
江西理工大学博士科研启动基金项目(2022205200100595),国家自然科学基金委员会项目(72261018),江西省教育厅青年项目(GJJ2200868)资助.Supported by the Jiangxi University of Science and Technology Doctoral Research Start-up Fund(2022205200100595),the National Natural Science Foundation of China(72261018)and the Science and Technology Research Project(GJJ2200868)from Jiangxi Education Department. （2022205200100595）
