HarvesSink: An Intelligent Greywater Quality Assessment and Diversion System for Domestic and Industrial Water Conservation

To autonomously classify and route kitchen wastewater for safe non-potable reuse, addressing water scarcity and inefficient domestic consumption.

Current solutions like manual diverters and basic filters lack real-time sensing or automation; HarvesSink provides intelligent multi-sensor evaluation and automated fail-safe diversion.

Domestic kitchen greywater reuse (e.g., garden irrigation, floor cleaning), industrial effluent treatment plant protection, and institutional settings like hostels/schools.

Harves Sink is an intelligent, under-sink greywater evaluation and diversion system designed to autonomously classify and route kitchen wastewater for safe non-potable reuse. The system integrates multi-parameter sensing across p H, Total Dissolved Solids (TDS), and turbidity with a deterministic embedded decision engine running on the STM 32 microcontroller platform, ac- tuation through solenoid valves, and a fail-safe architecture that defaults to drain under any anomalous condition. The first cohort of the i PD-CP program at IIIT-Delhi established the foundational engineering of the system, progressing from ideation and design thinking through proof-of-concept (Po C) validation and onward to subsystem-level engineering design approaching TRL-5. This second semester report documents the continuation of that work, covering the complete rebuild of the hardware prototype with 12 V pump-based diversion, rigorous multi-sample testing validating accuratewaterclassificationanddiversion, developmentofan Edge-AI softwarelayerincorporat- ing machine learning for prediction of laboratory-grade water quality metrics from inexpensive analogsensors, explorationandformalisationofanindustrialeffluenttreatmentusecase, partic- ipation in external competitions and incubation programmes, and iterative revision of the PCB schematic based on mentor feedback. The system has progressed to a stage where it performs real diversion of water samples using calibrated firmware logic, the ML layer is operational and generates predictions for Biological Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and Ammonia, andtheprojecthas received significant external validation through winning first prize at the Redbricks Hackathon 2025 at Ashoka University, receiving an incubation offer from IIIT-Delhi’s I-Hub Anubhuti, and entering preliminary discussions with an IIIT-Delhi incubated startup regarding the industrial ETP use case.