Enhancing hydrogen storage efficiency in LaNi4.75Al0.25-based metal hydride reactors using advanced machine learning techniques

Abstract

This study investigates the hydrogen storage efficiency of LaNi4.75Al0.25-based metal hydride reactors, incorporating advanced machine learning (ML) techniques to optimize design and performance. The research introduces scientific novelties by integrating ML algorithms such as AdaBoost, ANN, and Random Forest to predict hydrogen storage outcomes, classify reactor designs, and reduce experimental dependencies. Two reactor configurations-finned and un-finned-were experimentally evaluated, with finned designs demonstrating enhanced heat dissipation and hydrogen absorption rates due to increased thermal conductivity. Experimental findings revealed a fivefold improvement in heat transfer efficiency with fins, leading to faster absorption and greater storage capacity under high pressure. AdaBoost emerged as the most effective predictive model, achieving the lowest error metrics (MSE = 0.001, R2 = 0.983), underscoring ML's potential in reducing reliance on resource-intensive physical testing. The aluminum-modified LaNi5 alloy played a pivotal role in enhancing thermal management, ensuring sustainable and scalable hydrogen storage solutions. Comparative analysis validated the reactor's design superiority over conventional configurations, aligning with prior findings on advanced thermal management strategies. This study bridges existing gaps by combining ML-driven predictive analytics and innovative reactor designs, advancing theoretical knowledge and practical applications. Future work should focus on real-time data analytics, exploring additional ML techniques, and assessing industrial scalability to achieve robust, sustainable hydrogen storage systems. This integrated approach sets a new benchmark in hydrogen storage research, offering cost-effective, high-performance solutions for renewable energy systems.