Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks

Abstract

A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs and/or complex installation. The indirect measurement of a quantity can be used to obtain a desired variable, dispensing with the use of a specific sensor in the plant. Among the contributions of this technique is the design of the pressure controller using the adaptive control, as well as the use of an artificial neural network for the construction of nonlinear models using inherent system parameters such as pressure, engine rotation frequency and control valve angle, with the purpose of estimating the flow. Among the various contributions of the research, we can highlight the suppression in the acquisition of physical flow meters, the elimination of physical installation and others. The validation was carried out through tests in an experimental bench located in the Laboratory of Energy and Hydraulic Efficiency in Sanitation of the Federal University of Paraiba. The results of the soft sensor were compared with those of an electromagnetic flux sensor, obtaining a maximum error of 10%.

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

“…In Robson et al [18], a soft sensor was developed to estimate, based on pressure measurements, the flow in water distribution systems, as the relationship between these two physical variables is known. Two types of ANN were tested: MLP and NARX.…”

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

“…Modeling using ANNs is more efficient than other modeling techniques when the mathematical modeling of the system is complex and nonlinear, with hard modeling using differential equations, transfer functions, or the state space. Usually, this limitation is evidenced in nonlinear and time-varying systems, as in hydraulic processes, and extensive supply networks with many variables [ 26 , 27 ].…”

An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions

“…There are different approaches to soft sensor development, but the complexity of the production process and uncertainties in determining the connection between laboratory values and signals that are measured in the process are reasons why soft sensors in the process industry are mainly based on black box or gray box models. The black box approach is successfully applied to different processes, from the cement industry [ 1 , 2 , 3 , 4 ] and chemical processes [ 5 , 6 , 7 ] to water treatment [ 8 , 9 , 10 ], energy production [ 11 ] and the oil industry [ 12 , 13 , 14 ]. Since it proved appropriate for the development of soft sensors for a variety of industrial processes, the black box approach has the potential to be used as a basis for a wide industrial implementation of soft sensors.…”

“…Different model types have been used for the development of black box models for soft sensors in industrial processes, from models based on different types of Neural Networks [ 1 , 3 , 6 , 8 , 9 , 10 , 11 , 14 ], which represent the most common class of models, to Support Vector Regression [ 2 , 9 ], Neuro Fuzzy [ 13 ], Fuzzy Modeling [ 15 ], Linear Regression [ 16 ], and various other types of models. Determining the model type that is suitable for developing soft sensors for all industrial processes is, of course, not possible.…”