Long Jian, Chen Yifan, Cao Dengke, Chen Pengyu, Yang Minglei
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai200237, China.
ACS Omega. 2023 Feb 3;8(6):5437-5450. doi: 10.1021/acsomega.2c06624. eCollection 2023 Feb 14.
Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.
溶剂脱沥青（SDA）是一个复杂的多尺度连续过程。相关数据驱动模型未考虑SDA过程的运行模式。因此，本文提出一种具有多种运行模式的时滞过程预测模型来解决上述问题。首先，基于随机森林，研究了SDA过程中初始输入变量对脱油沥青（DAO）收率和康氏残炭的相对重要性，并根据结果选择特征。然后，使用堆栈去噪自动编码器（SDAE）对数据进行重构，获取SDAE隐藏层的非线性映射信息并实现特征降维。SDAE可提高模糊c均值的聚类精度，准确划分SDA过程的运行模式。使用长短期记忆网络（LSTM）建立多条件LSTM模型。与传统LSTM模型相比，多条件LSTM模型具有更高的预测精度，预测准确率>0.95。原料性质和操作条件对DAO收率的敏感性分析与SDA过程中的两相逆流萃取原理一致。此外，田纳西伊士曼过程的基准测试表明，所提方法在其他过程的故障检测中也有效。由于多条件LSTM可根据运行模式预测未来过程测量数据，因此能更好地避免误报问题并更早地预测故障。
