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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

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

Journal

The Journal is awaiting validation by the Administrative Services.

Title: PROCESSES

Final page: 409

ISSN: 2227-9717

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Associated Projects

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

Other information

Authenticus ID: 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

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16