Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

摘要

Abstract >Industrial processes are often characterized with high nonlinearities and dynamics. For soft sensor modelling, it is important to model the nonlinear and dynamic relationship between input and output data. Thus, long short‐term memory (LSTM) networks are suitable for quality prediction of soft sensor modelling. However, they do not consider the relevance of different input variables with the quality variable. To address this issue, a variable attention‐based long short‐term memory (VA‐LSTM) network is proposed for soft sensing in this paper. In VA‐LSTM, variable attention is designed to identify important input variables according to their relevance with quality prediction. After that, different attention weights are calculated and assigned to further obtain a weighted input sample at each time step. Finally, the LSTM network is exploited to capture the long‐term dependencies of the weighted input time series to predict the quality variable. The performance of the proposed modelling method is validated on an industrial debutanizer column and a hydrocracking process. </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> >工业过程通常具有高非线性和动态的特征。对于软传感器建模，重要的是要在输入和输出数据之间模拟非线性和动态关系。因此，长短短期存储器（LSTM）网络适用于软传感器建模的质量预测。但是，它们不考虑不同输入变量与质量变量的相关性。为了解决这个问题，提出了一种基于可变的关注的长短期存储器（VA-LSTM）网络，以便在本文中进行软感应。在VA-LSTM中，旨在根据其与质量预测的相关性来识别重要的输入变量。之后，计算并分配不同的注意重量，以在每次步骤中进一步获得加权输入样本。最后，利用LSTM网络来捕获加权输入时间序列的长期依赖性以预测质量变量。建议的建模方法的性能在工业脱金机柱和加氢裂化过程上验证。</ 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-30631/'>《The Canadian Journal of Chemical Engineering》</a> <b style="margin: 0 2px;">|</b><span>2020年第6期</span><b style="margin: 0 2px;">|</b><span>共13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/238.html" title="化学工业">化学工业;</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=attention&option=203" rel="nofollow">attention;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=deep learning&option=203" rel="nofollow">deep learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=long short‐term memory&option=203" rel="nofollow">long short‐term memory;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=prediction&option=203" rel="nofollow">prediction;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=soft sensor&option=203" rel="nofollow">soft sensor;</a> </p> <div class="translation"> 机译：注意;深入学习;长短期记忆;预测;软传感器; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704024776900.html">Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> </a> <a href="/journal-foreign-30631/" target="_blank" rel="nofollow" class="tuijian_authcolor">The Canadian Journal of Chemical Engineering .</a> <span>2020</span><span>,第6期</span> </span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: 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