Benesiktus Bao, Widi Aribowo, Ayusta Lukita Wardani , Aditya Chandra Hermawan

Control of DC Motor in Laundry Liquid Waste Treatment based on Esp32-S3 And Thingsboard Platform

Introduction

Control of dc motor in laundry liquid waste treatment based on esp32-s3 and thingsboard platform. Automate laundry wastewater treatment using an ESP32-S3 & ThingsBoard. Control DC motors for mixing & chemical feeding, reducing TSS & pH levels, with real-time IoT monitoring.

Abstract

Direct disposal of untreated laundry wastewater contributes to environmental pollution, with TSS (Total Suspended Solid) levels reaching 600 mg/L, far exceeding the 100 mg/L limit set by East Java Governor Regulation No. 72 of 2013. This research aims to develop an automated treatment system using an ESP32-S3 microcontroller integrated with pH, TSS, and temperature sensors, with real-time monitoring through the ThingsBoard platform. The DC motor serves as an actuator for the mixing and chemical feeding process. System testing showed the DC motor control had a 100% rate in processing TSS levels and 95% in reducing pH levels. On IoT data transmission, the average delay was 4 seconds for turbidity and 5 seconds for pH. Processing effectiveness was classified as 71% “Feasible,” 5% “Very Feasible,” and 19% “Less Feasible.” While there are some limitations, the system shows potential for adaptive wastewater treatment, which requires further improvements in sensor calibration and control reliability.

Review

This paper presents a timely and relevant solution to the critical environmental issue of untreated laundry wastewater discharge, which significantly exceeds regulatory limits. The authors propose an automated treatment system leveraging an ESP32-S3 microcontroller, integrated with pH, TSS, and temperature sensors, and the ThingsBoard platform for real-time monitoring. The core of the proposed system utilizes a DC motor as an actuator for crucial mixing and chemical feeding processes, aiming to provide an adaptive and remotely manageable wastewater treatment solution. The problem statement is well-articulated, underscoring the necessity of such a system in meeting environmental standards. The research demonstrates several commendable aspects, particularly the successful integration of IoT technology (ESP32-S3 and ThingsBoard) for real-time data acquisition and system control. The DC motor control exhibited robust performance, achieving a 100% rate in activating processes related to TSS levels and a 95% rate for pH level adjustments, indicating reliable actuator functionality. This integration of sensors with a programmable microcontroller and a cloud-based platform represents a significant step towards developing smart wastewater management systems that can be monitored and potentially adjusted remotely. The system's potential for adaptive wastewater treatment is a promising direction, offering flexibility in response to varying waste characteristics. However, the abstract also highlights areas that warrant further investigation and improvement. While the DC motor's operational reliability is high, the overall processing effectiveness, categorized as 19% "Less Feasible," suggests that the treatment outcome does not consistently meet desired standards, despite successful motor control. This discrepancy, along with the average IoT data transmission delays of 4-5 seconds, necessitates a deeper analysis into the causes of "Less Feasible" outcomes and potential optimizations for data latency. The authors acknowledge limitations in sensor calibration and control reliability, which are crucial for the practical deployment of any automated system. Future work should focus on elaborating on these specific limitations, improving calibration protocols, and refining the control algorithms to enhance the system's consistent effectiveness in reducing pollutants to acceptable levels.

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