Citation

Sungheetha, Akey and R., Rajesh Sharma and Mahendran, M and sakthi, D Vishnu and Karthikeyan, A and Kanmani, S and S, Lakshmi Saranya (2026) Quantum-Enhanced Electrochemical Water Splitting Technology with AI-Driven Optimization for Sustainable Hydrogen Production. Procedia Computer Science, 282. pp. 749-760. ISSN 1877-0509

Text 1-s2.0-S1877050926014602-main.pdf - Published Version Restricted to Repository staff only Download (451kB)

Abstract

The escalating global energy crisis and climate change necessitate sustainable hydrogen production technologies. Current electrochemical water splitting systems face significant challenges including high overpotential requirements, catalyst degradation, limited energy conversion efficiency averaging 65-70%, and inadequate real-time optimization capabilities. This research proposes a quantum-enhanced electrochemical water splitting framework integrated with AI-driven optimization to address these critical limitations. The methodology combines quantum computing algorithms for catalyst surface modeling with machine learning-based process optimization and real-time adaptive control mechanisms. The proposed system achieved hydrogen production efficiency of 87.3%, reduced overpotential by 34.2% to 1.48V, extended catalyst lifespan to 8,750 operational hours, and demonstrated energy conversion efficiency of 82.6%. Advanced deep learning models predicted optimal operating parameters with 94.8% accuracy while quantum algorithms reduced computational complexity for molecular dynamics simulations by 67.4%. The novelty lies in syner-gistic integration of quantum-classical hybrid computing, multi-objective reinforcement learning for dynamic optimization, and edge-computing enabled distributed control architecture. Experimental validation using synthetic datasets derived from thermody-namic models demonstrated superior performance compared to conventional approaches, with commercial viability for large-scale green hydrogen production facilities.

Item Type:	Article
Uncontrolled Keywords:	Quantum computing, Electrochemical water splitting, Hydrogen production, Machine learning optimization, Sustainable energy, Catalyst efficiency
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Applied Communication (FAC)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Sep 2026 03:34
Last Modified:	04 Sep 2026 03:34
URII:	http://shdl.mmu.edu.my/id/eprint/16701

Downloads

Edit (login required)