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.
2019-12-18
hybrid, modelling, steady-state, data reconciliation, machine learning
File in questo prodotto:
File Dimensione Formato  
Bassetto_Marco_1178614.pdf

Open Access dal 19/12/2022

Dimensione 3.94 MB
Formato Adobe PDF
3.94 MB Adobe PDF Visualizza/Apri

The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/28807