Smart water purification for domestic environments: AI-driven dual filtration with multilingual voice-assisted interaction
Year: 2026
Type: article
Abstract: Household water safety depends not only on contaminant removal efficiency but also on consistent user adherence and timely maintenance. However, usability barriers and lack of accessible guidance limit the real-world effectiveness of point-of-use purification systems, particularly in multilingual settings. This study presents Automated Quality Understanding and Voice Assistant i.e. AQUA-VA, a smart domestic water purifier integrating dual-stage RO + UV/UF filtration with edge artificial intelligence and multilingual voice-assisted interaction. The system employs low-power sensing of pH, total dissolved solids (TDS), turbidity, flow, and temperature, coupled with a machine-learning-based water quality index model and Bayesian filter health estimation for predictive maintenance. A mixed-integer programming scheduler optimizes energy usage and purification cycles, while an on-device speech interface enables real-time multilingual guidance. The system was evaluated over 12 weeks across 36 households under diverse water conditions. Results show a 71.3% reduction in unsafe water dispenses, a 54.8% decrease in delayed maintenance events, and a 26.4% improvement in first-attempt task completion compared to a baseline system without voice support. The speech module achieved 93.1% intent recognition accuracy with a median latency of 186 ms across multiple languages. These findings demonstrate that integrating edge AI with multilingual voice-assisted interaction significantly enhances water safety, usability, and maintenance adherence in household purification systems.
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204495677