IoT-Based Industrial Wastewater Monitoring System using ESP32 and Blynk
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Abstract
Effective industrial wastewater management is essential to mitigate the environmental and public health risks posed by harmful contaminants e.g. high TDS, turbidity, abnormal pH, and temperature. A monitoring system is crucial in each related company to ensure its wastewater will be properly treated to meet regulatory standards as that can 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, this study evaluates an Internet of Things (IoT)-based monitoring system for industrial wastewater, focusing on the measurement of four parameters, including total dissolved solids (TDS), pH, temperature, and turbidity. The system consisted of a Durian ESP32 microcontroller, a TDS sensor (SEN0244), a pH sensor (SEN0161), a turbidity sensor (SEN0189), and temperature sensor (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 testing in actual environment using textile dyeing industrial water samples. Results show that the proposed monitoring system was able to achieve measurement 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.
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