Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells.
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| Title: | Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells. |
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| Authors: | Xenitopoulos, Theofilos1,2 (AUTHOR), Papadopoulos, Athanasios I.3 (AUTHOR), Seferlis, Panos1 (AUTHOR) seferlis@auth.gr |
| Source: | Computers & Chemical Engineering. Aug2026, Vol. 211, pN.PAG-N.PAG. 1p. |
| Subjects: | System identification, Autoregressive models, Kalman filtering, Dynamic models, Autocorrelation (Statistics), Electrochemical analysis, PID controllers, Feedback control systems |
| Abstract: | • NARX model captures nonlinear dynamics of electrochemical CO 2 regeneration cells. • Partial Autocorrelation guides optimal NARX architecture selection. • Kalman filtering of experimental data enhances data quality for model tuning. • Tuned PI controller implemented for set point tracking and disturbance rejection. Electrochemical CO 2 regeneration is a promising alternative to conventional thermal processes, offering lower energy requirements and compatibility with renewable electricity. However, the dynamic behavior of electrochemical cells is complex, nonlinear, and not yet well understood, making robust control design challenging. This study proposes a multi-step, data-driven methodology for dynamic modeling and control of an electrochemical CO 2 regeneration cell, using experimental data from two cathode spacer geometries. Raw experimental signals were preprocessed using a Kalman filter to suppress high-frequency noise. Partial autocorrelation analysis guided the selection of autoregressive orders, and a systematic grid search identified optimal nonlinear autoregressive models with exogenous inputs (NARX). A comprehensive methodology is presented that connects data preprocessing, model architecture selection and controller optimization into a coherent pipeline. The best-performing models achieved a greater than 97% fit and root mean squared (RMSE) values below 0.005 for both geometries, accurately reproducing transient CO 2 flux responses to stepwise current excitation. The analysis revealed that the rhomboidal spacer cell configuration exhibits longer memory effects and allows more consistent system identification compared to the parallel spacer. Closed-loop control studies with a discrete-time PI controller, tuned via a genetic algorithm, demonstrated accurate set-point tracking, disturbance rejection, and safe operation under actuator saturation. These findings highlight that NARX-based system identification not only enables accurate control-oriented modeling but also provides new insights into how cell geometry influences dynamic behavior. [Display omitted] [ABSTRACT FROM AUTHOR] |
| Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193722989 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xenitopoulos%2C+Theofilos%22">Xenitopoulos, Theofilos</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Papadopoulos%2C+Athanasios+I%2E%22">Papadopoulos, Athanasios I.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Seferlis%2C+Panos%22">Seferlis, Panos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> seferlis@auth.gr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Chemical+Engineering%22">Computers & Chemical Engineering</searchLink>. Aug2026, Vol. 211, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22Autoregressive+models%22">Autoregressive models</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink><br /><searchLink fieldCode="DE" term="%22Autocorrelation+%28Statistics%29%22">Autocorrelation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electrochemical+analysis%22">Electrochemical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22PID+controllers%22">PID controllers</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • NARX model captures nonlinear dynamics of electrochemical CO 2 regeneration cells. • Partial Autocorrelation guides optimal NARX architecture selection. • Kalman filtering of experimental data enhances data quality for model tuning. • Tuned PI controller implemented for set point tracking and disturbance rejection. Electrochemical CO 2 regeneration is a promising alternative to conventional thermal processes, offering lower energy requirements and compatibility with renewable electricity. However, the dynamic behavior of electrochemical cells is complex, nonlinear, and not yet well understood, making robust control design challenging. This study proposes a multi-step, data-driven methodology for dynamic modeling and control of an electrochemical CO 2 regeneration cell, using experimental data from two cathode spacer geometries. Raw experimental signals were preprocessed using a Kalman filter to suppress high-frequency noise. Partial autocorrelation analysis guided the selection of autoregressive orders, and a systematic grid search identified optimal nonlinear autoregressive models with exogenous inputs (NARX). A comprehensive methodology is presented that connects data preprocessing, model architecture selection and controller optimization into a coherent pipeline. The best-performing models achieved a greater than 97% fit and root mean squared (RMSE) values below 0.005 for both geometries, accurately reproducing transient CO 2 flux responses to stepwise current excitation. The analysis revealed that the rhomboidal spacer cell configuration exhibits longer memory effects and allows more consistent system identification compared to the parallel spacer. Closed-loop control studies with a discrete-time PI controller, tuned via a genetic algorithm, demonstrated accurate set-point tracking, disturbance rejection, and safe operation under actuator saturation. These findings highlight that NARX-based system identification not only enables accurate control-oriented modeling but also provides new insights into how cell geometry influences dynamic behavior. [Display omitted] [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.compchemeng.2026.109677 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: System identification Type: general – SubjectFull: Autoregressive models Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Dynamic models Type: general – SubjectFull: Autocorrelation (Statistics) Type: general – SubjectFull: Electrochemical analysis Type: general – SubjectFull: PID controllers Type: general – SubjectFull: Feedback control systems Type: general Titles: – TitleFull: Nonlinear autoregressive network with exogenous inputs for dynamic modeling and control of electrochemical CO2 regeneration cells. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xenitopoulos, Theofilos – PersonEntity: Name: NameFull: Papadopoulos, Athanasios I. – PersonEntity: Name: NameFull: Seferlis, Panos IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00981354 Numbering: – Type: volume Value: 211 Titles: – TitleFull: Computers & Chemical Engineering Type: main |
| ResultId | 1 |
