Please use this identifier to cite or link to this item: http://dspace.aiub.edu:8080/jspui/handle/123456789/3064
Title: Sustainable Water Quality Monitoring for Efficient Industrial Water Management
Authors: Aziz, Shusmita Anjum
Ahmed, Tasnia
Patowary, Progga
Jubo, Md Kamruzzamn
Islam, Gazi Korbanul
Hossain, Md Sajid
Keywords: Industry
Wastewater
pH
TDS
Turbidity
Valve
Flow
Monitor
Management
Issue Date: Jun-2025
Publisher: IEEE
Citation: S. A. Aziz, T. Ahmed, P. Patowary, M. K. Jubo, G. K. Islam and M. S. Hossain, "Sustainable Water Quality Monitoring for Efficient Industrial Water Management," 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), 2025, pp. 1-6, doi: 10.1109/NCIM65934.2025.11160293.
Abstract: The rapid growth of industrialization has led to significant environmental pollution, particularly due to the discharge of untreated wastewater into rivers. This paper presented a microcontroller-based system designed to monitor and manage industrial effluents efficiently. The system incorporated pH, total dissolved solids (TDS), and temperature sensors to assess water quality and used solenoid valves controlled via a relay module to regulate water flow. When the pollution levels exceeded predefined thresholds, the system automatically diverted the contaminated water to a storage unit for recycling. If the water quality remained within acceptable limits, it was either released into natural water bodies or stored for future use. The prototype was tested using both clean and artificially polluted water samples, and in both cases, the system demonstrated accurate sensor readings and reliable valve control. Although the current version did not include a turbidity sensor, which would further enhance the detection of suspended particles, future improvements aimed to integrate this feature along with a purification mechanism for the stored water. Additionally, the system’s modularity and real-time operation make it adaptable to various industrial settings. This project offered a low-cost, automated, and scalable solution to help reduce water pollution, support better water resource management, and promote compliance with environmental regulations, making it particularly beneficial for industries aiming to implement sustainable wastewater management.
Description: Conference paper published in 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM). Contact email: sajidh@aiub.edu
URI: https://ieeexplore.ieee.org/document/11160293
http://dspace.aiub.edu:8080/jspui/handle/123456789/3064
ISBN: 979-8-3315-5542-9
Appears in Collections:Publications From Faculty of Engineering

Files in This Item:
File Description SizeFormat 
24_Sustainable_Water_Quality_Monitoring_for_Efficient_Industrial_Water_Ma.docx3.27 MBMicrosoft Word XMLView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.