A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

• Published in: Automation (Basel) Vol. 5; no. 2; pp. 106 - 127
• Main Authors: Alencar, Gabryel M. Raposo de, Fernandes, Fernanda M. Lima, Moura Duarte, Rafael, Melo, Petrônio Ferreira de, Cardoso, Altamar Alencar, Gomes, Heber Pimentel, Villanueva, Juan M. Mauricio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Artificial intelligence; Artificial neural networks; Control methods; Controllers; Costs; dropout; Energy consumption; Energy efficiency; Flow velocity; Industry 4.0; Multilayer perceptrons; Network topologies; Neural networks; Parameters; Penicillin; Sanitation; Sensors; soft sensor; Software; Time series; Uncertainty analysis; Variables; Water supply; Water supply systems
• ISSN: 2673-4052; 2673-4052
• DOI: 10.3390/automation5020008

The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical sensors in order to reduce costs and enhance efficiency. This study explores the implementation of an artificial neural network (ANN) based soft sensor model in a water supply system to predict flow rates within the system. The soft sensor is centered on a Long Short-Term Memory (LSTM) artificial neural network model using Monte Carlo dropout to reduce uncertainty and improve estimation performance. Based on the results of this work, it is concluded that the proposed soft sensor (with Monte Carlo dropout) can predict flow rates more precisely, contributing to the reduction in water losses, as well as cost savings. This approach offers a valuable solution for minimizing water losses and ensuring the efficient use of this vital resource. Regarding the use of soft sensors based on LSTM neural networks with a careful choice of Monte Carlo dropout parameters, when compared to the multilayer perceptron model, the LSTM model with Monte Carlo dropout showed better mean absolute error, root mean square error, and coefficient of determination: 0.2450, 0.3121, and 0.996437 versus 0.2556, 0.3522, and 0.9954. Furthermore, this choice of Monte Carlo dropout parameters allowed us to achieve an LSTM network model capable of reducing uncertainty to 1.8290, keeping the error metrics also at low levels.