Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor

Xingang Guo; Pei-Ying Hong; Taous-Meriem Laleg-Kirati

Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor

Xingang Guo, Pei-Ying Hong, Taous-Meriem Laleg-Kirati

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

Abstract

The optimal control and operation of a Membrane Bioreactor (MBR) process by Nonlinear Model Predictive Control (NMPC) is investigated in this work. First, the Benchmark Simulation Model for MBR (BSM-MBR) provided by Maere et al. (2011) to simulate a membrane bioreactor is extended to include a mathematical membrane fouling model where both reversible and irreversible fouling are taken into account. Then, an NMPC is designed by incorporating the nonlinear process model of BSM-MBR to control the dissolved oxygen concentration at certain level while meeting input and other process constraints. The performance of the NMPC is evaluated under both constant influent scenario and dynamic dry weather influent scenario. The simulation results demonstrate that NMPC works better in the constant influent case compared to the dynamic influent scenario.

Original language	English (US)
Title of host publication	Submitted to 21st IFAC World Congress
Publisher	Submitted to IFAC
State	Published - 2020

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@inproceedings{c02044c794ed4a28aca15e32fec8e68c,

title = "Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor",

abstract = "The optimal control and operation of a Membrane Bioreactor (MBR) process by Nonlinear Model Predictive Control (NMPC) is investigated in this work. First, the Benchmark Simulation Model for MBR (BSM-MBR) provided by Maere et al. (2011) to simulate a membrane bioreactor is extended to include a mathematical membrane fouling model where both reversible and irreversible fouling are taken into account. Then, an NMPC is designed by incorporating the nonlinear process model of BSM-MBR to control the dissolved oxygen concentration at certain level while meeting input and other process constraints. The performance of the NMPC is evaluated under both constant influent scenario and dynamic dry weather influent scenario. The simulation results demonstrate that NMPC works better in the constant influent case compared to the dynamic influent scenario.",

author = "Xingang Guo and Pei-Ying Hong and Taous-Meriem Laleg-Kirati",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): CARF-FCC/1/1971-32-01, BAS/1/1627-0101 Acknowledgements: This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1627-0101) to Taous Meriem Laleg and KAUST Office of Sponsored Research (OSR) under Awards No CARF-FCC/1/1971-32-01.",

year = "2020",

language = "English (US)",

booktitle = "Submitted to 21st IFAC World Congress",

publisher = "Submitted to IFAC",

}

TY - GEN

T1 - Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor

AU - Guo, Xingang

AU - Hong, Pei-Ying

AU - Laleg-Kirati, Taous-Meriem

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): CARF-FCC/1/1971-32-01, BAS/1/1627-0101 Acknowledgements: This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1627-0101) to Taous Meriem Laleg and KAUST Office of Sponsored Research (OSR) under Awards No CARF-FCC/1/1971-32-01.

PY - 2020

Y1 - 2020

N2 - The optimal control and operation of a Membrane Bioreactor (MBR) process by Nonlinear Model Predictive Control (NMPC) is investigated in this work. First, the Benchmark Simulation Model for MBR (BSM-MBR) provided by Maere et al. (2011) to simulate a membrane bioreactor is extended to include a mathematical membrane fouling model where both reversible and irreversible fouling are taken into account. Then, an NMPC is designed by incorporating the nonlinear process model of BSM-MBR to control the dissolved oxygen concentration at certain level while meeting input and other process constraints. The performance of the NMPC is evaluated under both constant influent scenario and dynamic dry weather influent scenario. The simulation results demonstrate that NMPC works better in the constant influent case compared to the dynamic influent scenario.

AB - The optimal control and operation of a Membrane Bioreactor (MBR) process by Nonlinear Model Predictive Control (NMPC) is investigated in this work. First, the Benchmark Simulation Model for MBR (BSM-MBR) provided by Maere et al. (2011) to simulate a membrane bioreactor is extended to include a mathematical membrane fouling model where both reversible and irreversible fouling are taken into account. Then, an NMPC is designed by incorporating the nonlinear process model of BSM-MBR to control the dissolved oxygen concentration at certain level while meeting input and other process constraints. The performance of the NMPC is evaluated under both constant influent scenario and dynamic dry weather influent scenario. The simulation results demonstrate that NMPC works better in the constant influent case compared to the dynamic influent scenario.

UR - http://hdl.handle.net/10754/661669

M3 - Conference contribution

BT - Submitted to 21st IFAC World Congress

PB - Submitted to IFAC

ER -

Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor

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