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Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process
[Image: see text] 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 probl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933188/ https://www.ncbi.nlm.nih.gov/pubmed/36816643 http://dx.doi.org/10.1021/acsomega.2c06624 |
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author | Long, Jian Chen, Yifan Cao, Dengke Chen, Pengyu Yang, Minglei |
author_facet | Long, Jian Chen, Yifan Cao, Dengke Chen, Pengyu Yang, Minglei |
author_sort | Long, Jian |
collection | PubMed |
description | [Image: see text] 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 R(2) > 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. |
format | Online Article Text |
id | pubmed-9933188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-99331882023-02-17 Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process Long, Jian Chen, Yifan Cao, Dengke Chen, Pengyu Yang, Minglei ACS Omega [Image: see text] 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 R(2) > 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. American Chemical Society 2023-02-03 /pmc/articles/PMC9933188/ /pubmed/36816643 http://dx.doi.org/10.1021/acsomega.2c06624 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Long, Jian Chen, Yifan Cao, Dengke Chen, Pengyu Yang, Minglei Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process |
title | Yield and Properties
Prediction Based on the Multicondition
LSTM Model for the Solvent Deasphalting Process |
title_full | Yield and Properties
Prediction Based on the Multicondition
LSTM Model for the Solvent Deasphalting Process |
title_fullStr | Yield and Properties
Prediction Based on the Multicondition
LSTM Model for the Solvent Deasphalting Process |
title_full_unstemmed | Yield and Properties
Prediction Based on the Multicondition
LSTM Model for the Solvent Deasphalting Process |
title_short | Yield and Properties
Prediction Based on the Multicondition
LSTM Model for the Solvent Deasphalting Process |
title_sort | yield and properties
prediction based on the multicondition
lstm model for the solvent deasphalting process |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933188/ https://www.ncbi.nlm.nih.gov/pubmed/36816643 http://dx.doi.org/10.1021/acsomega.2c06624 |
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