Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

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

Authors

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

Journal

The Journal is awaiting validation by the Administrative Services.

Title: PROCESSES

Final page: 250

ISSN: 2227-9717

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 7 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-1VB

DOI: 10.3390/pr10020250

Abstract (EN): A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18