Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Costa, EA (Autor) Outra; Rebello, CM (Autor) Outra; Santana, VV (Autor) Outra; Rodrigues, AE (Autor) Outra; Ana M. Ribeiro (Autor) FEUP; Schnitman, L (Autor) Outra; Nogueira, IBR (Autor) FEUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: PROCESSES

Página Final: 409

ISSN: 2227-9717

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Projetos Associados

LA LSRE-LCM - Financiamento Plurianual 2020-2023 para a Unidade LA LSRE-LCM Laboratório de Processos de Separação e Reacção - Laboratório de Catálise e Materiais

Outras Informações

ID Authenticus: P-00W-51W

DOI: 10.3390/pr10020409

Abstract (EN): Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time prediction of the productivity of a chemical

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16