Work

Enhanced Dual-Tag Coupled RFID Technology for Sensing Mixed Inorganic Salt Solutions: Incorporating the Impact of Water Velocity on Dielectric Measurements

Year: 2025

Type: article

Abstract: Accurate parameter estimation is essential for effective monitoring and treatment of high-salinity industrial wastewater. Traditional methods such as spectroscopy, ion chromatography, and electrochemical analysis offer high sensitivity but are often complex, costly, and unsuitable for real-time monitoring. This research integrates Deep Neural Networks (DNNs) with the Levenberg–Marquardt (LM) algorithm to develop an advanced RFID-based sensing system for real-time monitoring of sodium chloride solutions in wastewater. The DNN extracts essential features from raw data, while the LM algorithm optimizes parameter estimation for enhanced precision and stability. Experimental results show that the dielectric constant sample variance at various flow rates under wireless frequency is 0.08509, while the sample total variance is 0.06807, both below 0.1. Additionally, the sample standard deviation and total standard deviation are both below 0.3, at 0.26090 and 0.29169, respectively. These findings confirm that the proposed system is robust against flow rate variations, ensuring accurate, real-time monitoring and supporting sustainable industrial practices.

Source: Electronics

DOI: 10.3390/electronics14112124

Authors: Jiang Peng, Ammara Iqbal, Renhai Feng, Muhammad Zain Yousaf

Institutions: Tianjin University, Hubei University of Automotive Technology, Zhejiang University

Language: English

Topic: Microwave and Dielectric Measurement Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Clean water and sanitation, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896