文献类型：期刊文献

英文题名：Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

作者：Long, Jian[1];Chen, Yifan[1];Cao, Dengke[1];Chen, Pengyu[1];Yang, Minglei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

外文期刊名：ACS OMEGA

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000928772100001)】；

基金：This work was supported by National Key Research and Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800), National Natural Science Foundation of China (61973124, 62073142), Fundamental Research Funds for the Central Universities (222202317006), and Shanghai AI Lab.

语种：英文

摘要：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 R2 > 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.