Multi-objective optimization of bucket drum for lunar regolith collectors with multi-surrogate model based on adaptive invocation mechanism

Science China Technological Sciences, Journal Year: 2025, Volume and Issue: 68(5)

Published: April 16, 2025

Language: Английский

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Advancing Sustainable Energy Transition Through Green Hydrogen Valleys

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 31442 - 31471

Published: Jan. 1, 2025

Language: Английский

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How can current leakage Be reduced in protonic ceramic electrolysis cells? Insights from Thermo-electrochemical modeling

Journal of Power Sources, Journal Year: 2025, Volume and Issue: 642, P. 236957 - 236957

Published: April 8, 2025

Language: Английский

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Machine learning-driven prediction and optimization of selective glycerol electrocatalytic reduction into propanediols

Journal of Electroanalytical Chemistry, Journal Year: 2025, Volume and Issue: 988, P. 119150 - 119150

Published: April 25, 2025

Language: Английский

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Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

Atmosphere, Journal Year: 2024, Volume and Issue: 15(11), P. 1344 - 1344

Published: Nov. 9, 2024

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly modeling and predicting processes that impact air quality. This study focuses on hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions improving We developed two models using artificial neural networks (ANNs) support vector (SVM) to predict production. The input variables are current, voltage, communication delay time, real-time measured production, while output variable is at next sampling time. Both address critical issue hysteresis. Using 50 h SOEC system data, we evaluated effectiveness ANN SVM methods, incorporating time as an variable. results show model superior terms prediction performance. Specifically, shows strong predictive performance ε = 0.01–0.02 h, RMSE 2.59 × 10−2, MAPE 33.34 10−2%, MAE 1.70 10−2 Nm3/h, R2 99.76 10−2. At 0.03 yields 2.74 34.43 1.73 99.73 model, error values 2.70 44.01 2.24 99.74 they 2.67 43.44 2.11 99.75 With this precision, positive implications pollution control strategies development cleaner energy technologies, contributing overall improvements quality reduction pollutants.

Language: Английский

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Improvement of hydrogen reciprocating compressor efficiency: A novel capacity control system and its multi-objective optimization

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 92, P. 349 - 366

Published: Oct. 24, 2024

Language: Английский

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