Citation

Chia, Kim Seng and Choong, Jun Jie (2026) IoT-Based Industrial Wastewater Monitoring System using ESP32 and Blynk. Journal of Engineering Technology and Applied Physics, 8 (1). p. 106. ISSN 2682-8383

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Abstract

ective industrial wastewater management is essential to mitigate the environmental and public health risks posed by harmful contaminantse.g.high TDS, turbidity, abnormal pH, and temperature. A monitoring system is crucial in each related company to ensure itswastewater will beproperly treated to meet regulatory standardsas thatcan severely impact ecosystems, aquatic life, and water resources.However,traditional industrial wastewater monitoring methods like manual sampling and laboratory analysis fail to provide real-time data and are time consuming and labour intensive.Thus, thisstudy evaluates an Internet of Things (IoT)-based monitoring system for industrial wastewater, focusing on themeasurementof four parameters, including total dissolved solids (TDS), pH, temperature, and turbidity.The system consisted ofa Durian ESP32 microcontroller, a TDSsensor (SEN0244), a pHsensor (SEN0161), a turbiditysensor (SEN0189), and temperaturesensor (DS18B20). Real-time monitoring, data analysis, and visualization were facilitated via the Blynk cloud platform.The accuracy and reliability of the developed system were evaluated through functionality testing, performance testing, and system testingin actual environmentusing textile dyeingindustrial water samples. Results show that the proposed monitoring system was able to achievemeasurement accuracies of 87.76% for TDS, 93.28% for pH, and 95.35% for temperature.This shows that the system is feasible in continuously monitoring the TDS, pH, and temperature of water quality in industry.

Item Type:	Article
Uncontrolled Keywords:	IoT, real-time monitoring
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Jul 2026 04:28
Last Modified:	09 Jul 2026 04:28
URII:	http://shdl.mmu.edu.my/id/eprint/16361

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