Advanced AI Based Water Quality and Vending Machine
DOI:
https://doi.org/10.15662/IJEETR.2026.0802215Keywords:
Artificial Intelligence (AI), Water Quality Monitoring, Smart Vending Machine, Total Dissolved Solids (TDS), Real-Time Monitoring., Internet of Things (IoT)Abstract
Ensuring access to safe drinking water remains a significant issue in many areas due to rising pollution levels and the absence of continuous monitoring systems. Traditional water supply methods depend on periodic manual testing, which increases the risk of distributing contaminated water before detection. To overcome this limitation, an intelligent and automated solution is necessary for real-time water quality assessment and controlled distribution.
This project presents an advanced AI-based water quality monitoring and smart vending machine system aimed at delivering safe drinking water. It utilizes multiple sensors, including pH, turbidity, total dissolved solids (TDS), and water level sensors, to continuously evaluate essential water quality parameters. The sensor data is gathered and processed by a microcontroller, where an AI-based decision mechanism is applied to analyze the results.
Based on the analysis, the system determines whether the water is suitable for consumption. If all parameters fall within safe limits, the vending system is activated to dispense water. Otherwise, the system restricts access and displays a warning message. Additional components such as an LCD display, keypad, and relay module enhance user interaction and automation.
The proposed system offers real-time monitoring, improved efficiency, and higher reliability compared to conventional methods. It is well-suited for deployment in public locations like schools, hospitals, railway stations, and rural areas to ensure safe and accessible drinking water
References
1. A. Kumar and B. Singh, “Smart Water Quality Monitoring System Using IoT,” International Journal of Engineering Research, vol. 8, no. 4, pp. 120–125, 2020.
2. S. R. Nayak et al., “Machine Learning Techniques for Water Quality Prediction,” IEEE Access, vol. 7, pp. 123456–123465, 2019.
3. S. R. Nayak et al., “Machine Learning Techniques for Water Quality Prediction,” IEEE Access, vol. 7, pp. 123456–123465, 2019.
4. P. Sharma, “IoT-Based Smart Water Vending Machine,” Journal of Embedded Systems, vol. 10, no. 2, pp. 78–85, 2022.
5. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
6. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
7. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
8. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
10. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
11. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
12. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
13. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
14. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
15. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
16. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
17. R. K. Sharma and S. Gupta, “IoT-Based Smart Water Quality Monitoring System,” International Journal of Advanced Research in Computer Science, vol. 11, no. 3, pp. 210–215, 2020.
18. L. Zhang, Y. Wang, and X. Liu, “Real-Time Water Quality Prediction Using Machine Learning Techniques,” Journal of Environmental Informatics, vol. 35, no. 2, pp. 98–107, 2021.
19. T. Nguyen and H. Tran, “Smart Water Distribution System with Quality Monitoring and Control,” IEEE International Conference on Smart Cities, pp. 150–155, 2022.
20. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
21. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
22. Rajasekar, M. (2025). Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks. International Journal of Computer Technology and Electronics Communication, 8(4), 11078-11086.
23. Anujaa, T., Thajudeen Ali Ahamed, A. F., Baranwal, V., Thanikaiselvan, V., Subashanthini, S., Sivaranjani Devi, C., & Rengarajan, A. (2025). A lightweight multi round confusion-diffusion cryptosystem for securing images using a modified 5D chaotic system. Scientific Reports, 15(1), 31986.
24. Ramanathan, U., & Rajendran, S. (2023). Weighted particle swarm optimization algorithms and power management strategies for grid hybrid energy systems. Engineering Proceedings, 59(1), 123.
