TY - GEN
T1 - A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation System Modeled by Differential Algebraic Equations
AU - Wang, Yubin
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.
UR - http://www.scopus.com/inward/record.url?scp=85144593301&partnerID=8YFLogxK
U2 - 10.1109/CCTA49430.2022.9966101
DO - 10.1109/CCTA49430.2022.9966101
M3 - Conference contribution
AN - SCOPUS:85144593301
T3 - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
SP - 1271
EP - 1277
BT - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Y2 - 23 August 2022 through 25 August 2022
ER -
