Fault detection and diagnosis for reactive distillation based on convolutional neural network
Fault detection and diagnosis for reactive distillation based on convolutional neural network
复制标题
基于卷积神经网络的反应精馏故障检测与诊断
DOI：
10.1016/j.compchemeng.2020.107172
复制
发表时间：
2021-01-06
影响因子：
4.3
通讯作者：
Liu, Botong
中科院分区：
文献类型：
--
作者：
Ge, Xiaolong;Wang, Beibei;Liu, Botong
反应蒸馏（RD）在实现过程强化方面显示出其优势。然而，集成在RD中的复杂现象通常导致各种异常操作状态，例如催化剂失活。虽然控制方案已被设计来解决一些干扰，诊断的运行状态在线是至关重要的，以有效地避免严重的事故。以甲酸强化生产过程为基准，首次采用随机算法对控制结构进行优化设计，并进行动态试验验证控制结构的有效性。然后考虑13个实际故障，并模拟相应的响应。该方法综合考虑时间和空间两个域的特征，利用含有测量噪声的历史动态过程数据构造样本，并在此基础上训练和验证深度卷积神经网络。利用t-SNE将各层的机器学习信息可视化，故障诊断率表明了该方法的有效性。(C)2020爱思唯尔有限公司保留所有权利。
Reactive distillation (RD) shows its strength in achieving process intensification. However, the complex phenomena integrated in RD usually leads to various abnormal operating states, e.g. catalyst deactivation. Although control schemes have been designed to tackle some disturbances, diagnosing the operating state online is of vital importance for effectively avoiding serious accidents. In the present work, by using intensified process for formic acid production as benchmark, optimal design with stochastic algorithm was firstly performed and dynamic test was carried out to validate effectiveness of control structure. Then thirteen practical faults were considered and the corresponding response was simulated. By considering features in both spatial and temporal domain, historical dynamic process data with measurement noise was used to formulate samples, based on which deep convolutional neural network was trained and validated. The machine learning information in each layer was visualized using t-SNE and fault diagnosis rate shows the significance of the method. (C) 2020 Elsevier Ltd. All rights reserved.