MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202621073583 A) filed by Prashant Sheshrao Titare; Dr. P. Malathi; Chinmay Surendra Bhintade; Ruchir Sanjit Sukhatankar; and Shreya Chandrakant Patil on June 13, 2026, for Wateriq: Ai-Powered Greywater Monitoring, Reuse Scoring, And Filter Health Prediction System.
Inventors include Prashant Sheshrao Titare; Dr. P. Malathi; Chinmay Surendra Bhintade; Ruchir Sanjit Sukhatankar; and Shreya Chandrakant Patil.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: Water scarcity and inefficient water usage have become critical global concerns, particularly in urban households where a significant portion of water is discharged as greywater without reuse. Addressing this gap requires systems that are not only intelligent but also cost-effective and scalable. The proposed system, WaterIQ: AI- Powered Water Reuse Scoring and Filter Health Analysis, introduces a practical and server-centric solution for real-time greywater monitoring and reuse recommendation. The system is designed around a low-cost IoT sensing unit built using an ESP32 microcontroller integrated with multiple water quality sensors, including pH, turbidity, total dissolved solids (TDS/EC), and temperature sensors. These sensors continuously collect real time data from domestic greywater sources such as sinks and laundry outlets. Instead of relying on complex on-site processing, the collected data is transmitted as structured JSON payloads over Wi-Fi to a centralized backend server, ensuring minimal hardware complexity and improved scalability. At the core of the system lies a Flask-based backend that functions as the intelligence layer. It receives incoming sensor data through RESTful APIs, validates and stores the data in structured CSV files and cloud storage for long-term analysis. The backend further integrates machine learning models such as Random Forest, which analyses water quality parameters to classify greywater into predefined reuse categories, including Garden, Flush, Washroom, Drinking, and Unsafe. In addition to classification, the system generates a quantitative Reuse Score ranging from 0 to 100, simplifying complex chemical data into an intuitive metric for end users. A key innovation of this system is its ability to predict filter clogging conditions without requiring dedicated hardware sensors. By analysing trends, anomalies, and pressure-related patterns in the collected data, the backend provides early warnings for maintenance, thereby enhancing system reliability and reducing operational costs. The user interface is delivered through an Android mobile application that communicates with the backend using Retrofit-based APIs. The application provides a real-time dashboard displaying live sensor readings, reuse category, reuse score, clogging status, and timestamps. It also includes graphical visualization of historical data trends, enabling users to monitor changes in water quality over time and make informed reuse decisions. Overall, the WaterIQ system establishes a complete end-to-end pipeline, transforming greywater into actionable insights through sensing, cloud processing, machine learning, and intuitive visualization. By centralizing intelligence and minimizing hardware requirements, the system offers a scalable, cost- efficient, and user-friendly solution for sustainable water management in residential environments.
Disclaimer: Curated by HT Syndication.
