Design and Implementation of an IoT-Based Electrocoagulation System for Textile Wastewater Color Degradation
Abstract
This paper presents the design and implementation of an electrocoagulation system for the degradation of color pollutants in textile wastewater. The system utilizes electrochemical reactions at metal electrodes to generate hydroxide flocs that bind and remove dissolved contaminants. It integrates pH, turbidity, and water level sensors to automate operation, while an ESP32-based controller enables remote monitoring and control via the Internet of Things (IoT). A hybrid power configuration, combining solar energy and grid electricity, is employed to enhance energy efficiency and sustainability. Experimental results indicate that the system can effectively reduce color intensity and chemical oxygen demand (COD) by more than 90% under optimal voltage conditions (10–60 V). The proposed design supports Sustainable Development Goal (SDG) 6: Clean Water and Sanitation by offering a low-cost, compact, and decentralized wastewater treatment alternative for small and medium textile industries. Keywords—Electrocoagulation, textile wastewater, color degradation, IoT monitoring, hybrid energy, SDG 6.
Downloads
References
M. M. Baig and H. Gholamhosseini, “Smart health monitoring systems: an overview of design and modeling,” J Med Syst, vol. 37, no., Apr. 2013, doi: 10.1007/S10916-012-9898-Z.
M. M. Alam, H. Malik, M. I. Khan, T. Pardy, A. Kuusik, and Y. le
Moullec, “A survey on the roles of communication technologies in IoT-Based personalized healthcare applications,” IEEE Access, vol. 6, pp. 36611–36631, Jul. 2018, doi: 10.1109/ACCESS.2018.2853148.
E. Bazrafshan, L. Mohammadi, A. Ansari-Moghaddam, and A. H. Mahvi, “Heavy metals removal from aqueous environments by electrocoagulation process - A systematic review,” Oct. 26, 2015, BioMed Central Ltd. doi: 10.1186/s40201-015-0233-8. S. Li, L. da
Xu, and X. Wang, “A continuous biomedical signal acquisition system based on compressed sensing in body sensor networks,” IEEE Trans Industr Inform, vol. 9, no.
, pp. 1764–1771, 2013, doi: 10.1109/TII.2013.2245334.
P. Rashidi and A. Mihailidis, “A survey on ambient-assisted living tools for older adults,” IEEE J Biomed Health Inform, vol. 17, no. 3, pp. 579–590, 2013, doi: 10.1109/JBHI.2012.2234129.
A. Arcelus, R. Goubran, M. H. Jones, and F. Knoefel, “Integration of smart home technologies in a health monitoring system for the elderly,” Proceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW’07, vol. 1, pp. 820–825, 2007, doi: 1109/AINAW.2007.209.
A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensorbased systems for health monitoring and prognosis,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 40, no. 1, pp. 1–12, 2010, doi: 1109/TSMCC.2009.2032660.
M. E. Garbelini et al., “SweynTooth: Unleashing Mayhem over Bluetooth Low Energy”, Accessed: May 31, 2022. [Online]. Available: https://www.usenix.org/conference/atc20/presentation/garbelini A. S. Seferagi´c et al., “Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things,” Sensors 2020, Vol. 20, Page 488, vol. 20, no. 2, p. 488, Jan. 2020, doi: 10.3390/S20020488.
V. S. Thomas, S. Darvesh, C. MacKnight, and K. Rockwood, “Estimating the Prevalence of Dementia in Elderly People: A
Comparison of the Canadian Study of Health and Aging and National
Population Health Survey Approaches,” Int Psychogeriatr, vol. 13, no.
S1, pp. 169–175, 2001, doi: 10.1017/S1041610202008116.
S. Majumder, T. Mondal, and M. J. Deen, “Wearable Sensors for Remote Health Monitoring,” Sensors (Basel), vol. 17, no. 1, Jan. 2017, doi: 10.3390/S17010130.
