On-device artificial intelligence agent based on language models for electrochemical water desalination
Year: 2026
Type: article
Abstract: • Edge-deployed LLM integrates electrochemical knowledge for control and prediction • Validated against 320 published desalination studies • Hybrid LLM–machine learning enables robust prediction under missing sensor inputs • Vision-language retrieval reduces hallucinations with full reference accuracy • Enables privacy-preserving, low-cost decentralized automation Electrochemical water treatment is essential for tackling global water scarcity but remains difficult to optimize due to limited expertise and computing resources at many treatment facilities. Here, we introduce an intelligent on-device platform that combines electrochemical process knowledge with large language models deployed directly on edge devices such as Raspberry Pi. This system integrates theoretical understanding with real-time optimization, eliminating the need for cloud connectivity while ensuring data privacy and accessibility. Tested against 320 published studies, it achieves a 60 % reduction in hallucination rates and maintains strong predictive accuracy (R² > 0.80) for key variables such as effluent concentration and energy, even with incomplete sensor inputs. Notably, prediction accuracy for challenging parameters, such as the applied current (the driving force for electrochemical water desalination), improves from 0.03 to 0.63. By bringing intelligence to the data rather than sending it to the cloud, this approach makes advanced water-treatment intelligence feasible in resource-limited, data-imperfect, decentralized environments where physics-based models cannot be deployed.
Source: Water Research
Authors: Zahid Ullah, Hoo Hugo Kim, Moon Son
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,660
OpenAlex ID: W7155085271