Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
This work addresses operational efficiency in wastewater treatment for utilities, but it is incremental as it applies existing forecast-and-optimize methods to a new domain.
The paper reviews a digital twin applied to wastewater treatment at Urban Utility to enhance operational efficiency, though it does not provide specific numerical results.
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.