This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures.

The paper presents a numerical analysis of air flow through flow channels in unipolar PEM fuel cell: serpentine, parallel, pins. Simulations were performed at flow rates of 100 ml/min and 300 ml/min with an operating temperature of 80°C to evaluate the models based on flow distribution, pressure drop and velocity magnitude. The results show that serpentine provides sufficient velocity for the gas transportation and water management, but it has a issue related to pressure drop, being the highest from all three models. Parallel model minimizes the pressure drop, but it has a non-uniform flow on the parallel channels. Even though there are low velocity regions in the pin model, it is a balanced solution between flow distribution, pressure drop and velocity magnitude. Using CFD numerical modelling helps in analyzing the flow through unipolar plates before manufacturing and testing, leading to cost reduction and design optimization.