Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911849925050368 |
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| author | Colwell, Matthew Abolghasemi, Mahdi |
| author_facet | Colwell, Matthew Abolghasemi, Mahdi |
| contents | Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems. While prediction is concerned with estimating the unknown future values of a variable, optimisation is concerned with optimising the decision given all the available data. These methods are used together to solve problems for sequential decision-making where often we need to predict the future values of variables and then use them for determining the optimal decisions. This paradigm is known as forecast and optimise and has numerous applications, e.g., forecast demand for a product and then optimise inventory, forecast energy demand and schedule generations, forecast demand for a service and schedule staff, to name a few. In this extended abstract, we review a digital twin that was developed and applied in wastewater treatment in Urban Utility to improve their operational efficiency. While the current study is tailored to the case study problem, the underlying principles can be used to solve similar problems in other domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14635 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment Colwell, Matthew Abolghasemi, Mahdi Machine Learning Artificial Intelligence Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems. While prediction is concerned with estimating the unknown future values of a variable, optimisation is concerned with optimising the decision given all the available data. These methods are used together to solve problems for sequential decision-making where often we need to predict the future values of variables and then use them for determining the optimal decisions. This paradigm is known as forecast and optimise and has numerous applications, e.g., forecast demand for a product and then optimise inventory, forecast energy demand and schedule generations, forecast demand for a service and schedule staff, to name a few. In this extended abstract, we review a digital twin that was developed and applied in wastewater treatment in Urban Utility to improve their operational efficiency. While the current study is tailored to the case study problem, the underlying principles can be used to solve similar problems in other domains. |
| title | Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.14635 |