Green hydrogen production is emerging as a crucial component in global decarbonization efforts. This review focuses on the role of computational approaches and artificial intelligence (AI) in optimizing green hydrogen technologies. Key approaches to improving electrolyzer efficiency and scalability include computational fluid dynamics (CFD), thermodynamic modeling, and machine learning (ML). As an instance, CFD has achieved over 95% accuracy in estimating flow distribution and polarization curves, but AI-driven optimization can lower operational expenses by up to 24%. Proton exchange membrane electrolyzers achieve efficiencies of 65–82% at 70–90 °C, but solid oxide electrolyzers reach up to 90% efficiency at temperatures ranging from 650 to 1000 °C. According to studies, combining renewable energy with hydrogen production reduces emissions and improves grid reliability, with curtailment rates of less than 1% for biomass-driven systems. This integration of computational approaches and renewable energy ensures a long-term transition to green hydrogen while also addressing energy security and environmental concerns.

A Modified Firefly Algorithm for Optimal Sizing and Siting of Voltage Controlled Distributed Generators in Distribution Networks

In this paper, open loop and closed loop Tunneling Magnetoresistance (TMR) DC Current Transformers (DCCTs) for ion beam diagnostics are presented. The DCCTs employ MR sensors to measure the DC component of the accelerator’s ion beam. A comparative study between Giant Magnetoresistance (GMR) and TMR sensors is presented to illustrate the sensor selection criterion for the DCCT application. The two proposed DCCTs are studied in open and closed loop configurations. A closed loop feedback electronic system is designed to generate a feedback current equivalent to the ion beam current such that the sensor operates at zero flux. Furthermore, theoretical and experimental results for the TMR-based DCCT including noise analysis are presented for both open loop and closed loop configurations. Both configurations’ minimum detectable currents are in the range of microampere. The proposed closed loop hardware prototype has a settling time of less than 15 μs. The measured minimum detectable currents for the open and closed loop TMR-based DCCTs are 128.2 μA/$\sqrt{\mathrm{Hz}}$ and 10.14 μA/$\sqrt{\mathrm{Hz}}$ at 1 Hz, respectively.