Estimation and Control of WRRF Biogas Production
The development of resource-efficient digital technologies is a critical challenge in the wastewater sector. This industrial case study, conducted in collaboration with the Veas Water Resource Recovery Facility in Norway, focused on creating data pre-processing methods and resource-efficient control...
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2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5922 |
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| author | Tiina M. Komulainen Kjell Rune Jonassen Simen Gjelseth Antonsen |
| author_facet | Tiina M. Komulainen Kjell Rune Jonassen Simen Gjelseth Antonsen |
| author_sort | Tiina M. Komulainen |
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| description | The development of resource-efficient digital technologies is a critical challenge in the wastewater sector. This industrial case study, conducted in collaboration with the Veas Water Resource Recovery Facility in Norway, focused on creating data pre-processing methods and resource-efficient control strategies. Using data from the Veas biogas plant, dynamic models were developed to compare control outcomes. The primary objective was to maximize biogas production and hot water usage while maintaining optimal temperature and hydraulic retention time by adjusting inlet sludge and hot water flow rates. Sequential operations were approximated as continuous operations using a 30-min moving minimum/maximum for bimodal data and a 2-h moving average for noisy data. The data-driven dynamic models achieved an accuracy of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.85. The control strategy, which included one feedback controller, one ratio controller, and flow rate restrictions, was compared to real production data (baseline) and tested across six scenarios. The best improvement over the baseline scenario resulted in a 3% increase in total biogas production, a 6% increase in total organic loading, a 13% increase in hot water use, and a one-day reduction in hydraulic retention time. Future work should focus on control studies using extended datasets and nonlinear models. |
| format | Article |
| id | doaj-art-7c94d2c8998249ffaa40e40ad59ad0ed |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-7c94d2c8998249ffaa40e40ad59ad0ed2024-12-13T16:25:24ZengMDPI AGEnergies1996-10732024-11-011723592210.3390/en17235922Estimation and Control of WRRF Biogas ProductionTiina M. Komulainen0Kjell Rune Jonassen1Simen Gjelseth Antonsen2Department of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University, Pilestredet 35, 0130 Oslo, NorwayVeas AS, Bjerkåsholmen 125, 3470 Slemmestad, NorwayDepartment of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University, Pilestredet 35, 0130 Oslo, NorwayThe development of resource-efficient digital technologies is a critical challenge in the wastewater sector. This industrial case study, conducted in collaboration with the Veas Water Resource Recovery Facility in Norway, focused on creating data pre-processing methods and resource-efficient control strategies. Using data from the Veas biogas plant, dynamic models were developed to compare control outcomes. The primary objective was to maximize biogas production and hot water usage while maintaining optimal temperature and hydraulic retention time by adjusting inlet sludge and hot water flow rates. Sequential operations were approximated as continuous operations using a 30-min moving minimum/maximum for bimodal data and a 2-h moving average for noisy data. The data-driven dynamic models achieved an accuracy of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.85. The control strategy, which included one feedback controller, one ratio controller, and flow rate restrictions, was compared to real production data (baseline) and tested across six scenarios. The best improvement over the baseline scenario resulted in a 3% increase in total biogas production, a 6% increase in total organic loading, a 13% increase in hot water use, and a one-day reduction in hydraulic retention time. Future work should focus on control studies using extended datasets and nonlinear models.https://www.mdpi.com/1996-1073/17/23/5922biogasWater Resource Recovery Facilityestimationdynamic modelingcontrol strategy development |
| spellingShingle | Tiina M. Komulainen Kjell Rune Jonassen Simen Gjelseth Antonsen Estimation and Control of WRRF Biogas Production Energies biogas Water Resource Recovery Facility estimation dynamic modeling control strategy development |
| title | Estimation and Control of WRRF Biogas Production |
| title_full | Estimation and Control of WRRF Biogas Production |
| title_fullStr | Estimation and Control of WRRF Biogas Production |
| title_full_unstemmed | Estimation and Control of WRRF Biogas Production |
| title_short | Estimation and Control of WRRF Biogas Production |
| title_sort | estimation and control of wrrf biogas production |
| topic | biogas Water Resource Recovery Facility estimation dynamic modeling control strategy development |
| url | https://www.mdpi.com/1996-1073/17/23/5922 |
| work_keys_str_mv | AT tiinamkomulainen estimationandcontrolofwrrfbiogasproduction AT kjellrunejonassen estimationandcontrolofwrrfbiogasproduction AT simengjelsethantonsen estimationandcontrolofwrrfbiogasproduction |