Development of a Real-Time Water Quality Monitoring System Using Internet of Things (IoT) Technology: A Case Study of the Oxidation Ditch Wastewater Treatment System at Bamrasnaradura Infectious Diseases Institute - Tatong, Thatamon; Pichaironaphak, Janista (Journal of Bamrasnaradura Infectious Diseases Institute, 2026)
Oxidation Ditch wastewater treatment systems are biological treatment systems that play an important role in maintaining effluent quality in healthcare facilities. However, conventional water quality monitoring based on periodic sampling and laboratory analysis has limitations in providing continuous surveillance and timely detection of abnormal conditions. This study aimed to (1) develop a real-time water quality monitoring system using Internet of Things (IoT) technology for the Oxidation Ditch wastewater treatment system at Bamrasnaradura Infectious Diseases Institute, (2) evaluate the performance of the developed system, and (3) assess its potential to support wastewater treatment management. This research and development study was conducted in four phases: problem analysis and system design; equipment development and installation; system testing and calibration; and field evaluation. The developed system consisted of four architectural layers: a sensing layer, a data communication layer using Modbus RS485, a processing layer using a Programmable Logic Controller (PLC), and a data storage and visualization layer using Cloud technology through a Human–Machine Interface (HMI) and dashboard. Data were continuously collected from October 2025 to May 2026 and compared with laboratory analytical results to evaluate the agreement and measurement errors of the IoT system. The developed system successfully transmitted and displayed water quality data in real time, with data completeness of 98.26% and system availability of 99.89%. Comparison with laboratory results showed that pH measurements had a mean absolute error (MAE) of 0.10 pH units and a mean absolute percentage error (MAPE) of 1.35%; however, the correlation was not statistically significant (r = 0.478, p = 0.231). In contrast, TDS measurements showed a very strong positive correlation with laboratory results (r = 0.968, p < 0.001) and a MAPE of 2.88%. Field evaluation demonstrated that the system enabled continuous monitoring of water quality trends and system operating status, while supporting real-time alerts and data-informed wastewater treatment control. The developed IoT-based monitoring system demonstrated potential for continuous water quality surveillance and for supporting wastewater treatment management in healthcare facilities. The system may also serve as a technological foundation for the development of smart environmental monitoring systems in healthcare settings.