Data-Driven Temperature and Catalyst Optimization in Hydrogen Production using K-means Clustering

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The optimization of hydrogen production as part of the transition to sustainable energy presents a challenge as the interplay of operational parameters decides the production rate such as temperature and the reaction performance of a catalyst. Typically, efficiency gains are sought through trial-and-error experimentation, or using linear models, which cannot model nonlinear relationships or high dimensional interactions. These issues must be dealt with advanced, data-driven techniques to uncover hidden patterns in large data sets. In this research K-means clustering, a machine learning technique, is used to analyze and optimize hydrogen production processes. The study used a dataset of 980 records derived from experimental results to discover five distinct catalyst behavior clusters based on different temperature conditions. The methodology consisted of data preprocessing, exploratory analysis, and model training and it was validated using the Elbow Method and silhouette scores. At the end, the optimal cluster configuration resulted in an SSD of 150.23 and a silhouette score of 0.72, suggesting good-quality clustering. We then used cluster analysis to derive operational patterns including temperature ranges for which certain catalysts operate at their optimum and unique performance profiles, to make more precise data-driven recommendations. Results show that machine learning is novel and superior to prior approaches to this problem, offering additional insight than traditional statistical methods. The approach allows for higher precision in the selection of the catalyst and in the process, optimization employing the identification of different operational profiles. Additional variables, like pressure and reaction times, could be added to this framework for future research, or adaptive clustering models could be used to improve real-time production optimization. This work demonstrates the utility of machine learning in the development of efficient and sustainable systems to produce hydrogen.