An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials

摘要

Abstract >The surface‐free lithium content, which is a critical index that reflecting the quality of ternary cathode materials, usually cannot be monitored in real time due to technical restriction. To this end, soft sensor technique is applied to predict the surface‐free lithium content by readily available process variables, making timely control on operation parameters. In this paper, a modeling method based on ensemble just‐in‐time learning (JITL) soft‐sensor is developed. In the model, data feature indices are firstly designed to properly extract the real‐time and short‐time features between the batching and the loading procedure. Accordingly, ensemble JITL soft‐sensor model is constructed with the semisupervised local weighted probability principal component regression. Moreover, considering varying working conditions, an adaptive moving window technique is adopted to improve the adaptability of the model. The validation and the flexibility of the developed modeling method are testified with the practical manufacturing data. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >无表面锂含量，这是反映三元阴极材料质量的关键指数，通常不能在技术限制中实时监测。为此，应用软传感器技术通过容易获得的工艺变量来预测无表面锂含量，并对操作参数进行及时控制。在本文中，开发了一种基于集合仲计学学习（JITL）软传感器的建模方法。在该模型中，首先设计数据功能指标以正确提取批处理和加载过程之间的实时和短时特征。因此，集合JITL软传感器模型由半培育的局部加权概率主成分回归构成。此外，考虑到不同的工作条件，采用自适应移动窗口技术来改善模型的适应性。验证和开发建模方法的灵活性以实际的制造数据作证。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-33917/'>《Journal of Chemometrics》</a> <b style="margin: 0 2px;">|</b><span>2020年第5期</span><b style="margin: 0 2px;">|</b><span>共16页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/159.html" title="化学">化学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=just‐in‐time learning&option=203" rel="nofollow">just‐in‐time learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=moving window&option=203" rel="nofollow">moving window;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=prediction of residual lithium content&option=203" rel="nofollow">prediction of residual lithium content;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=semisupervised learning&option=203" rel="nofollow">semisupervised learning;</a> </p> <div class="translation"> 机译：即时学习;移动窗口;预测残留锂含量;半质化学习; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div 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