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

doi:10.1109/CCTA49430.2022.9966101

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 proceeding › Conference contribution › peer-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 language	English (US)
Title of host publication	2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1271-1277
Number of pages	7
ISBN (Electronic)	9781665473385
DOIs	https://doi.org/10.1109/CCTA49430.2022.9966101
State	Published - 2022
Event	2022 IEEE Conference on Control Technology and Applications, CCTA 2022 - Trieste, Italy Duration: Aug 23 2022 → Aug 25 2022

Publication series

Name	2022 IEEE Conference on Control Technology and Applications, CCTA 2022

Conference

Conference	2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Country/Territory	Italy
City	Trieste
Period	08/23/22 → 08/25/22

Bibliographical note

Funding Information:
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Fund (BAS/1/1627-01-01).

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CCTA49430.2022.9966101

Cite this

Wang, Y., Marani, Y., & Laleg Kirati, T. M. (2022). A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations. In 2022 IEEE Conference on Control Technology and Applications, CCTA 2022 (pp. 1271-1277). (2022 IEEE Conference on Control Technology and Applications, CCTA 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCTA49430.2022.9966101

Wang, Yubin ; Marani, Yasmine ; Laleg Kirati, Taous Meriem. / A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations. 2022 IEEE Conference on Control Technology and Applications, CCTA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1271-1277 (2022 IEEE Conference on Control Technology and Applications, CCTA 2022).

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

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.",

author = "Yubin Wang and Yasmine Marani and {Laleg Kirati}, {Taous Meriem}",

note = "Funding Information: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Fund (BAS/1/1627-01-01). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Conference on Control Technology and Applications, CCTA 2022 ; Conference date: 23-08-2022 Through 25-08-2022",

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doi = "10.1109/CCTA49430.2022.9966101",

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Wang, Y, Marani, Y & Laleg Kirati, TM 2022, A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations. in 2022 IEEE Conference on Control Technology and Applications, CCTA 2022. 2022 IEEE Conference on Control Technology and Applications, CCTA 2022, Institute of Electrical and Electronics Engineers Inc., pp. 1271-1277, 2022 IEEE Conference on Control Technology and Applications, CCTA 2022, Trieste, Italy, 08/23/22. https://doi.org/10.1109/CCTA49430.2022.9966101

A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations. / Wang, Yubin; Marani, Yasmine; Laleg Kirati, Taous Meriem.
2022 IEEE Conference on Control Technology and Applications, CCTA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1271-1277 (2022 IEEE Conference on Control Technology and Applications, CCTA 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Marani, Yasmine

AU - Laleg Kirati, Taous Meriem

N1 - Funding Information: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Fund (BAS/1/1627-01-01). Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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Y2 - 23 August 2022 through 25 August 2022

ER -

Wang Y, Marani Y, Laleg Kirati TM. A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations. In 2022 IEEE Conference on Control Technology and Applications, CCTA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1271-1277. (2022 IEEE Conference on Control Technology and Applications, CCTA 2022). doi: 10.1109/CCTA49430.2022.9966101

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

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

UN SDGs

Access to Document

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