Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction
This study builds upon a hierarchical control strategy for ammonium that regulates the dissolved oxygen set-point in wastewater treatment plants. First, the study aims to improve the ammonium sensor measurement by considering its noise and delay. Noise in ammonium measurement can lead to incorrect c...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2454700 |
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| author | I. Santín M. Meneses C. Pedret M. Barbu R. Vilanova |
| author_facet | I. Santín M. Meneses C. Pedret M. Barbu R. Vilanova |
| author_sort | I. Santín |
| collection | DOAJ |
| description | This study builds upon a hierarchical control strategy for ammonium that regulates the dissolved oxygen set-point in wastewater treatment plants. First, the study aims to improve the ammonium sensor measurement by considering its noise and delay. Noise in ammonium measurement can lead to incorrect control actions by controllers, which can have negative consequences on the aquatic ecosystem, and greater wear on the actuators. Filters are applied with the main goal of reducing variations in ammonium measurement and, consequently, in the actuator, while improving dissolved oxygen set-point tracking. However, filters cause a delay in measurement and therefore also in the controllers' performance, reducing the effects of control strategies on environmental objectives. Thus, ammonium measurement errors are predicted to correct the delay caused by the filters. Finally, adaptive controls are proposed to vary the proportional gain of the controller when there is a change in dissolved oxygen set-point, aiming for a faster actuator response to set-point changes. For the filters, a weighted average filter and an event-based filter are proposed. For ammonium measurement error prediction, linear regressions and artificial neural networks are suggested. Finally, two adaptive controls are applied based on the filter used. Both vary the proportional gain of the controller based on the set-point variation, with one of them being activated only when an event is detected. Various combinations of the proposed techniques were tested, reducing abrupt actuator variations and achieving the maximum integral of squared error reduction of 74% in ammonium measurement error, and 88% in dissolved oxygen control. |
| format | Article |
| id | doaj-art-eb4c3ba09812481da63a154083ec16ef |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-eb4c3ba09812481da63a154083ec16ef2025-01-21T03:56:32ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2025.2454700Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error predictionI. Santín0M. Meneses1C. Pedret2M. Barbu3R. Vilanova4Department of Telecommunications and Systems Engineering, School of Engineering, Autonomous University of Barcelona, Barcelona, SpainDepartment of Telecommunications and Systems Engineering, School of Engineering, Autonomous University of Barcelona, Barcelona, SpainDepartment of Telecommunications and Systems Engineering, School of Engineering, Autonomous University of Barcelona, Barcelona, SpainDepartment of Automatic Control and Electrical Engineering, “Dunarea de Jos” University of Galati, Galati, RomaniaDepartment of Telecommunications and Systems Engineering, School of Engineering, Autonomous University of Barcelona, Barcelona, SpainThis study builds upon a hierarchical control strategy for ammonium that regulates the dissolved oxygen set-point in wastewater treatment plants. First, the study aims to improve the ammonium sensor measurement by considering its noise and delay. Noise in ammonium measurement can lead to incorrect control actions by controllers, which can have negative consequences on the aquatic ecosystem, and greater wear on the actuators. Filters are applied with the main goal of reducing variations in ammonium measurement and, consequently, in the actuator, while improving dissolved oxygen set-point tracking. However, filters cause a delay in measurement and therefore also in the controllers' performance, reducing the effects of control strategies on environmental objectives. Thus, ammonium measurement errors are predicted to correct the delay caused by the filters. Finally, adaptive controls are proposed to vary the proportional gain of the controller when there is a change in dissolved oxygen set-point, aiming for a faster actuator response to set-point changes. For the filters, a weighted average filter and an event-based filter are proposed. For ammonium measurement error prediction, linear regressions and artificial neural networks are suggested. Finally, two adaptive controls are applied based on the filter used. Both vary the proportional gain of the controller based on the set-point variation, with one of them being activated only when an event is detected. Various combinations of the proposed techniques were tested, reducing abrupt actuator variations and achieving the maximum integral of squared error reduction of 74% in ammonium measurement error, and 88% in dissolved oxygen control.https://www.tandfonline.com/doi/10.1080/21642583.2025.2454700Wastewater treatment plantsevent-based filterevent-based adaptive controlartificial neural networkslinear regression |
| spellingShingle | I. Santín M. Meneses C. Pedret M. Barbu R. Vilanova Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction Systems Science & Control Engineering Wastewater treatment plants event-based filter event-based adaptive control artificial neural networks linear regression |
| title | Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction |
| title_full | Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction |
| title_fullStr | Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction |
| title_full_unstemmed | Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction |
| title_short | Improving hierarchical ammonium control operation in wastewater treatment with non-ideal sensors and actuators using filters, adaptive control and measurement error prediction |
| title_sort | improving hierarchical ammonium control operation in wastewater treatment with non ideal sensors and actuators using filters adaptive control and measurement error prediction |
| topic | Wastewater treatment plants event-based filter event-based adaptive control artificial neural networks linear regression |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2025.2454700 |
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