A probabilistic model-based approach to assess and minimize scaling in geothermal plants
Abstract Geothermal installations often face operational challenges related to scaling which can lead to loss in production, downtime, and an increase in operational costs. To accurately assess and minimize the risks associated with scaling, it is crucial to understand the interplay between geotherm...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Geothermal Energy |
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| Online Access: | https://doi.org/10.1186/s40517-025-00336-7 |
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| author | Pejman Shoeibi Omrani Jonah Poort Eduardo G. D. Barros Hidde de Zwart Cintia Gonçalves Machado Laura Wasch Aris Twerda Huub H. M. Rijnaarts Shahab Shariat Torbaghan |
| author_facet | Pejman Shoeibi Omrani Jonah Poort Eduardo G. D. Barros Hidde de Zwart Cintia Gonçalves Machado Laura Wasch Aris Twerda Huub H. M. Rijnaarts Shahab Shariat Torbaghan |
| author_sort | Pejman Shoeibi Omrani |
| collection | DOAJ |
| description | Abstract Geothermal installations often face operational challenges related to scaling which can lead to loss in production, downtime, and an increase in operational costs. To accurately assess and minimize the risks associated with scaling, it is crucial to understand the interplay between geothermal brine composition, operating conditions, and pipe materials. The accuracy of scaling predictive models can be impacted by uncertainties in the brine composition, stemming from sub-optimal sampling of geothermal fluid, inhibitor addition, or measurement imprecision. These uncertainties can be further increased for fluid at extreme conditions especially high salinity and temperature. This paper describes a comprehensive method to determine operational control strategies to minimize the scaling considering brine composition uncertainties. The proposed modelling framework to demonstrate the optimization under uncertainty workflow consists of a multiphase flow solver coupled with a geochemistry model and an uncertainty quantification workflow to locally estimate the probability of precipitation potential, including its impact on the hydraulic efficiency of the geothermal plant by increasing the roughness and/or decreasing the diameter of the casings and pipelines. For plant operation optimization, a robust control problem is formulated with scenarios which are generated based on uncertainties in brine composition using an exhaustive search method. The modelling and optimization workflow was demonstrated in a geothermal case study dealing with barite and celestite scaling in a heat exchanger. The results showed the additional insights in the potential impact of brine composition uncertainties (aleatoric uncertainties) in scaling potential and precipitation location. Comparing the outcome of optimization problem for the deterministic and fluid composition uncertainties, a change of up to 2.5% in the temperature control settings was observed to achieve the optimal coefficient of performance. |
| format | Article |
| id | doaj-art-9325b996b2194b8bad6755cb207de5bd |
| institution | Kabale University |
| issn | 2195-9706 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geothermal Energy |
| spelling | doaj-art-9325b996b2194b8bad6755cb207de5bd2025-02-02T12:15:36ZengSpringerOpenGeothermal Energy2195-97062025-01-0113112410.1186/s40517-025-00336-7A probabilistic model-based approach to assess and minimize scaling in geothermal plantsPejman Shoeibi Omrani0Jonah Poort1Eduardo G. D. Barros2Hidde de Zwart3Cintia Gonçalves Machado4Laura Wasch5Aris Twerda6Huub H. M. Rijnaarts7Shahab Shariat Torbaghan8Environmental Technology, Wageningen University & ResearchHeat Transfer and Fluid Dynamics, TNOApplied Geosciences, TNOHeat Transfer and Fluid Dynamics, TNOApplied Geosciences, TNOApplied Geosciences, TNOHeat Transfer and Fluid Dynamics, TNOEnvironmental Technology, Wageningen University & ResearchEnvironmental Technology, Wageningen University & ResearchAbstract Geothermal installations often face operational challenges related to scaling which can lead to loss in production, downtime, and an increase in operational costs. To accurately assess and minimize the risks associated with scaling, it is crucial to understand the interplay between geothermal brine composition, operating conditions, and pipe materials. The accuracy of scaling predictive models can be impacted by uncertainties in the brine composition, stemming from sub-optimal sampling of geothermal fluid, inhibitor addition, or measurement imprecision. These uncertainties can be further increased for fluid at extreme conditions especially high salinity and temperature. This paper describes a comprehensive method to determine operational control strategies to minimize the scaling considering brine composition uncertainties. The proposed modelling framework to demonstrate the optimization under uncertainty workflow consists of a multiphase flow solver coupled with a geochemistry model and an uncertainty quantification workflow to locally estimate the probability of precipitation potential, including its impact on the hydraulic efficiency of the geothermal plant by increasing the roughness and/or decreasing the diameter of the casings and pipelines. For plant operation optimization, a robust control problem is formulated with scenarios which are generated based on uncertainties in brine composition using an exhaustive search method. The modelling and optimization workflow was demonstrated in a geothermal case study dealing with barite and celestite scaling in a heat exchanger. The results showed the additional insights in the potential impact of brine composition uncertainties (aleatoric uncertainties) in scaling potential and precipitation location. Comparing the outcome of optimization problem for the deterministic and fluid composition uncertainties, a change of up to 2.5% in the temperature control settings was observed to achieve the optimal coefficient of performance.https://doi.org/10.1186/s40517-025-00336-7Uncertainty quantificationRobust optimizationScalingPrecipitationGeothermal production |
| spellingShingle | Pejman Shoeibi Omrani Jonah Poort Eduardo G. D. Barros Hidde de Zwart Cintia Gonçalves Machado Laura Wasch Aris Twerda Huub H. M. Rijnaarts Shahab Shariat Torbaghan A probabilistic model-based approach to assess and minimize scaling in geothermal plants Geothermal Energy Uncertainty quantification Robust optimization Scaling Precipitation Geothermal production |
| title | A probabilistic model-based approach to assess and minimize scaling in geothermal plants |
| title_full | A probabilistic model-based approach to assess and minimize scaling in geothermal plants |
| title_fullStr | A probabilistic model-based approach to assess and minimize scaling in geothermal plants |
| title_full_unstemmed | A probabilistic model-based approach to assess and minimize scaling in geothermal plants |
| title_short | A probabilistic model-based approach to assess and minimize scaling in geothermal plants |
| title_sort | probabilistic model based approach to assess and minimize scaling in geothermal plants |
| topic | Uncertainty quantification Robust optimization Scaling Precipitation Geothermal production |
| url | https://doi.org/10.1186/s40517-025-00336-7 |
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