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

Authors :
Alirio Rodrigues
Erbet Almeida Costa
Carine Rebello
Leizer Schnitman
Ana Ribeiro
Idelfonso B. R. Nogueira
Vinicius Santana
Source :
Processes; Volume 10; Issue 2; Pages: 409
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

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 process. The results demonstrate that the proposed methodology can yield a soft sensor based on DL that provides reliable predictions, with precision being proven by its corresponding coverage region.

Details

Language :
English
ISSN :
22279717
Database :
OpenAIRE
Journal :
Processes; Volume 10; Issue 2; Pages: 409
Accession number :
edsair.doi.dedup.....ce880e5c5483211ec6a89d32a864d6f5
Full Text :
https://doi.org/10.3390/pr10020409