Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes

Dan Yang, Xin Peng, Zhencheng Ye, Yusheng Lu and Weimin Zhong

Applied Energy, 2021, vol. 303, issue C, No S030626192100979X

Abstract: Real-time monitoring of quality index especially energy consumption is of great significance to improve energy efficiency and save energy. In ethylene distillations, the energy consumption cannot be measured online due to the lack of corresponding online analytical instruments and soft sensing is an effective technology to solve the problem. Additionally, ethylene industrial processes often operate under multiple conditions due to the change of the working conditions, in which the insufficient data will lead to poor prediction performances of the soft sensing model. Therefore, in this paper, a novel soft sensing method based on transfer learning framework is proposed to deal with the above problems, which consists of a feature extractor and a predictor. Concretely, to reduce the difference of domain distribution in the feature extractor, the lower bound estimation of Wasserstein distance is proposed as a metric of the difference. In the predictor, to mitigate the performance degradation caused by the domain difference, the adaptive features for predictor are constructed by the source domain features and uncertainty modeling of the quantified domain difference, which is measured through the proposed manifold distance. Therefore, an adaptive transfer learning framework is proposed to reduce effect of the domain difference, named as Domain Adaptation Network with Uncertainty Modeling (DANUM). Finally, a soft sensing method based on DANUM is proposed to predict quality index and energy consumption in ethylene distillation processes, which shows the effectiveness of our method.

Keywords: Transfer learning; Domain adaptation; Wasserstein distance; Uncertainty modeling; Soft sensing; Ethylene distillation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192100979X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:303:y:2021:i:c:s030626192100979x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.117610

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().