A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations

Yubin Wang, Yasmine Marani, Taous Meriem Laleg Kirati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to its high rejection rate and low energy consumption, Direct Contact Membrane Distillation (DCMD) technology is drawing more attention for seawater desalination, to meet the urgent and growing demands for freshwater. State estimation in DCMD system, which is modeled by nonlinear Differential Algebraic Equations (DAE) is crucial for controller design and system's monitoring. In this paper, a novel learning-based observer is proposed for state estimation of the DCMD system. The method consists of an encoder and decoder structure. The encoder allows to transform the DAE system into a linear ODE modulo an output injection in the latent space and the decoder helps in recovering the state estimate from the latent state. First, a brief description of the DCMD system and its DAE model are recalled. Then, the method is presented and illustrated. Explanations on how the learning structures are constructed and trained are provided. Finally, numerical simulations are conducted to illustrate the effectiveness of the proposed learning-based observer design.

Original languageEnglish (US)
Title of host publication2022 IEEE Conference on Control Technology and Applications, CCTA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1271-1277
Number of pages7
ISBN (Electronic)9781665473385
DOIs
StatePublished - 2022
Event2022 IEEE Conference on Control Technology and Applications, CCTA 2022 - Trieste, Italy
Duration: Aug 23 2022Aug 25 2022

Publication series

Name2022 IEEE Conference on Control Technology and Applications, CCTA 2022

Conference

Conference2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Country/TerritoryItaly
CityTrieste
Period08/23/2208/25/22

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Control and Systems Engineering
  • Control and Optimization

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