Internet of Things (IoT) Implementation in Electrocoagulation Method for Wastewater Treatment: A Case Study on Part Per Million (PPM) Analysis

Household waste is still underutilized, largely due to the lack of technological implementation in household wastewater treatment. The potential of technology to offer a solution for the efficient, cheap, and affordable utilization of wastewater is examined. The focus is placed on the implementation of a TDS (Total Dissolve Solid) sensor monitoring system, which is designed to measure the level of solids in water (ppm). The ppm monitoring process is carried out by dipping the TDS sensor probe, with the readings being displayed on an LCD monitor via I2C. Programming on the ESP32 is used to make the sensor data accessible through the Blynk application. To validate the readings, a TDS meter is employed to compare the amount of solids (ppm) and determine the accuracy of the sensor readings and application. After three tests were conducted, the initial readings from the measuring instrument were recorded as 48 ppm, 38 ppm, and 1100 ppm, while the results were 250 ppm, 275 ppm, and 285 ppm. The TDS sensor results were obtained as 79 ppm, 33 ppm, and 1089 ppm, with final readings being 259 ppm, 284 ppm, and 291 ppm. Average differences between the measuring instruments and sensors were calculated as 21.1 mg/L, 4.9 mg/L, and 42.3 mg/L. However, after testing for 3 hours, the solid levels were found to meet the Hygienic Sanitation standards in Ministry Health of Indonesia No. 32 of 2017, which is ≤ 500 ppm. The value of ppm shown that water is safe to consume for daily life.

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Published
2024-12-11
DOI
https://doi.org/10.1109/isriti64779.2024.10963446
Primary Topic
Internet of Things and AI
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article
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article

Internet of Things (IoT) Implementation in Electrocoagulation Method for Wastewater Treatment: A Case Study on Part Per Million (PPM) Analysis

Ikhsan Kamil, Wisnu Hendri Mulyadi, Ajeng Bening Kusumaningtyas, Denish Denish et al.
Internet of Things and AI
article

Internet of Things (IoT) Implementation in Electrocoagulation Method for Wastewater Treatment: A Case Study on Part Per Million (PPM) Analysis

Ikhsan Kamil, Wisnu Hendri Mulyadi, Ajeng Bening Kusumaningtyas, Denish Denish, Alfin Syaghof Azuri, Bintang Rahman Hakim
article en

Abstract

Household waste is still underutilized, largely due to the lack of technological implementation in household wastewater treatment. The potential of technology to offer a solution for the efficient, cheap, and affordable utilization of wastewater is examined. The focus is placed on the implementation of a TDS (Total Dissolve Solid) sensor monitoring system, which is designed to measure the level of solids in water (ppm). The ppm monitoring process is carried out by dipping the TDS sensor probe, with the readings being displayed on an LCD monitor via I2C. Programming on the ESP32 is used to make the sensor data accessible through the Blynk application. To validate the readings, a TDS meter is employed to compare the amount of solids (ppm) and determine the accuracy of the sensor readings and application. After three tests were conducted, the initial readings from the measuring instrument were recorded as 48 ppm, 38 ppm, and 1100 ppm, while the results were 250 ppm, 275 ppm, and 285 ppm. The TDS sensor results were obtained as 79 ppm, 33 ppm, and 1089 ppm, with final readings being 259 ppm, 284 ppm, and 291 ppm. Average differences between the measuring instruments and sensors were calculated as 21.1 mg/L, 4.9 mg/L, and 42.3 mg/L. However, after testing for 3 hours, the solid levels were found to meet the Hygienic Sanitation standards in Ministry Health of Indonesia No. 32 of 2017, which is ≤ 500 ppm. The value of ppm shown that water is safe to consume for daily life.

Jakarta State Polytechnic (ID)
Clean water and sanitation
Openalex Percentile: Top 53%
Internet of Things and AI
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Internet of Things (IoT) Implementation in Electrocoagulation Method for Wastewater Treatment: A Case Study on Part Per Million (PPM) Analysis — Ikhsan Kamil, Wisnu Hendri Mulyadi, et al. · (2024) | TGRS Research Map | TGRS