In the process industry, it is possible to encounter systems whose behavior cannot be mapped through a first principles (white-box) model. Hybrid models aim at integrating data- driven (black-box) elements within white-box process models in order to fill the gap between the white-model model predictions and the actual system response. The goal of this Thesis is to propose and implement a hybrid modelling framework, and to assess its performance with respect to a white-box model.
Steady-state detection, data reconciliation and machine learning for hybrid process modelling
Bassetto, Marco
2019/2020
Abstract
In the process industry, it is possible to encounter systems whose behavior cannot be mapped through a first principles (white-box) model. Hybrid models aim at integrating data- driven (black-box) elements within white-box process models in order to fill the gap between the white-model model predictions and the actual system response. The goal of this Thesis is to propose and implement a hybrid modelling framework, and to assess its performance with respect to a white-box model.File in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/28807