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Machine learning-based prediction and optimization of solid oxide electrolysis cells for green hydrogen production using RF, FFNN, and PSO approaches

Ibrahim Shomope, Amani Al-Othman, Muhammad Tawalbeh, Hussam Alshraideh

2025Energy5 citationsDOI

Topics & Concepts

Hydrogen productionElectrolyteParticle swarm optimizationHydrogenElectrolysisResponse surface methodologyComputer sciencePolymer electrolyte membrane electrolysisMaterials scienceVolume (thermodynamics)Process engineeringCathodeSupport vector machineCurrent (fluid)Biological systemOxideArtificial neural networkElectrodeVoltageProduction (economics)Relevance vector machineDimensioningFeature vectorFeature (linguistics)AnodeParticle (ecology)AlgorithmHigh-temperature electrolysisOhmic contactCurrent densityAdvancements in Solid Oxide Fuel CellsChemical Looping and Thermochemical ProcessesHybrid Renewable Energy Systems
Machine learning-based prediction and optimization of solid oxide electrolysis cells for green hydrogen production using RF, FFNN, and PSO approaches | Litcius