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dc.contributor.authorGuo, Xin-Gang
dc.contributor.authorHong, Pei-Ying
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2021-01-31T02:21:20Z
dc.date.available2021-01-31T02:21:20Z
dc.date.issued2021
dc.date.submitted2020-06-14
dc.identifier.citationGuo, X.-G., Hong, P.-Y., & Laleg-Kirati, T.-M. (2021). Calibration and validation for a real-time membrane bioreactor: A sliding window approach. Journal of Process Control, 98, 92–105. doi:10.1016/j.jprocont.2020.11.013
dc.identifier.issn0959-1524
dc.identifier.doi10.1016/j.jprocont.2020.11.013
dc.identifier.urihttp://hdl.handle.net/10754/667090
dc.description.abstractThe paper presents a novel model calibration and validation strategy of membrane bioreactor (MBR) for wastewater treatment. The approach is based on a dynamic model of the activated sludge process and it consists simultaneously on estimating the model's parameters and computing the dissolved oxygen control input. Activated sludge model No. 1 (ASM1) has been widely used to describe the biological process of activated sludge processes. However, most system states and parameters within ASM1 are not easily obtained, hence not applicable for model calibration and validation. In this work, a reduced-order model presented herein serves as a tool for predicting the dynamic behavior of the MBR plant. The model contains only 4 measurable states, where 13 parameters need to be identified. To reduce the complexity of the calibration, the static sensitivity analysis is performed to select the sensitive parameters. The selected parameters are identified based on directly measurable real-time data obtained from the plant. In addition, the dissolved oxygen is also maintained at a certain level to mimic the real-time control behavior. Model calibration is achieved based on a sliding window optimization problem, which searches for the optimal parameters set and control variables during each identification cycle. Different datasets sampled for the same MBR plant have been used for model validation. Both calibration and validation results are evaluated by several performance indexes, which indicates an acceptable correspondence with the experimental data. The developed model can be employed for process state estimation and control purpose as well as design issues for MBR systems.
dc.description.sponsorshipThis work has been supported by the KAUST, Saudi Arabia baseline (BAS/1/1627-0101) and Center of Excellence for NEOM research at KAUST, Saudi Arabia flagship project research fund (REI/1/4178-03-01) awarded to Taous Meriem Laleg.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S095915242030336X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in [JournalTitle]. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in [JournalTitle], [98, , (2021)] DOI: 10.1016/j.jprocont.2020.11.013 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleCalibration and validation for a real-time membrane bioreactor combined with dissolved oxygen control
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering Division (CEMSE), Saudi Arabia, King Abdullah University of Science and Technology (KAUST)
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Process Control
dc.rights.embargodate2023-01-11
dc.eprint.versionPost-print
dc.identifier.volume98
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.pages92-105
pubs.publication-statusAccepted
kaust.personGuo, Xin-Gang
kaust.personHong, Pei-Ying
kaust.personLaleg-Kirati, Taous-Meriem
kaust.grant.numberBAS/1/1627-0101
kaust.grant.numberREI/1/4178-03-01
dc.date.accepted2020-11-26
dc.identifier.eid2-s2.0-85099201034
refterms.dateFOA2021-01-31T02:21:21Z
kaust.acknowledged.supportUnitCenter of Excellence for NEOM research at KAUST


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