Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 145, С. 110210 - 110210

Опубликована: Фев. 20, 2025

Язык: Английский

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Design and optimization of a mechanical metamaterial featuring dual tunability in auxeticity and bandgap modulation

Composite Structures, Год журнала: 2025, Номер unknown, С. 119050 - 119050

Опубликована: Март 1, 2025

Язык: Английский

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AI-Driven Design Optimization for Sustainable Buildings: A Systematic Review

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115440 - 115440

Опубликована: Фев. 1, 2025

Язык: Английский

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Machine learning model for predicting long-term energy consumption in buildings

Discover Internet of Things, Год журнала: 2025, Номер 5(1)

Опубликована: Март 3, 2025

Язык: Английский

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Automatic Information Gain-guided Convergence for refining building design parameters: Enhancing effectiveness and interpretability in Simulation-based Optimization

Building and Environment, Год журнала: 2025, Номер unknown, С. 112788 - 112788

Опубликована: Март 1, 2025

Язык: Английский

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Design optimization of climate-responsive rural residences in solar rich areas considering sustainability and occupant comfort

Energy and Buildings, Год журнала: 2025, Номер 336, С. 115546 - 115546

Опубликована: Март 5, 2025

Язык: Английский

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Machine Learning‐Driven Multi‐Objective Optimization of Microchannel Reactors for CO₂ Conversion

Advanced Sustainable Systems, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

Abstract Recently, the power‐to‐gas (PtG) concept, specifically thermocatalytic CO₂ conversion via Sabatier process, emerges as a promising route for mitigating greenhouse gas emissions. The process transforms and H₂ into methane water under low‐temperature methanation conditions. This study suggests new way to improve performance of microchannel reactor by combining computational fluid dynamics (CFD), response surface methodology (RSM), machine learning (ML), multi‐objective optimization. Key design variables include inlet velocity, temperature, channel length ratios. RSM approach is generating datasets simulation; while, data augmentation assists ML model training. Six models—linear, ensemble, tree, Gaussian, support vector (SVM), neural networks are evaluated regression accuracy against RSM‐based correlation. Gaussian found superior integrated with optimization algorithm. A decision‐making score (DMS) levels normalizes indicators. It finds best designs rates ≈78.6% CH₄ selectivity close 99.9%. These results demonstrate an advanced significantly reducing demand (24 h 1.471 ms) CFD simulations; maintaining accuracy, thereby enabling cost‐effective, efficient solutions across various engineering applications in real‐world PtG applications.

Язык: Английский

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What lies ahead? The future performance of Global South residential buildings amid climate change: A systematic literature review

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111486 - 111486

Опубликована: Дек. 1, 2024

Язык: Английский

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