Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 83, P. 1 - 12

Published: Aug. 8, 2024

Language: Английский

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Sustainable Freshwater/Energy Supply through Geothermal-Centered Layout Tailored with Humidification-Dehumidification Desalination Unit; Optimized by Regression Machine Learning Techniques

Energy, Journal Year: 2024, Volume and Issue: 303, P. 131919 - 131919

Published: June 3, 2024

Language: Английский

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Harnessing Biomass Energy: Advancements through Machine Learning and AI Applications for Sustainability and Efficiency

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 191, P. 193 - 205

Published: Aug. 24, 2024

Language: Английский

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Experimental simulation and analysis of Acacia Nilotica biomass gasification with XGBoost and SHapley Additive Explanations to determine the importance of key features

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136291 - 136291

Published: April 1, 2025

Language: Английский

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Optimizing hydrogen-rich gas production by steam gasification with integrated CaO-based adsorbent materials for CO2 capture: Machine learning approach

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 95, P. 695 - 709

Published: Nov. 21, 2024

Language: Английский

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Role of Machine Learning and Artificial Intelligence in Biofuel/Bioenergy Productions

Clean Energy Production Technologies, Journal Year: 2025, Volume and Issue: unknown, P. 375 - 398

Published: Jan. 1, 2025

Language: Английский

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Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Industrial & Engineering Chemistry Research, Journal Year: 2024, Volume and Issue: 63(44), P. 18940 - 18956

Published: Oct. 25, 2024

Carbon materials possess active sites and functionalities on the surface that can attract prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to predict optimized methane uptake, adsorption energy (Ead), adsorbent rediscovery through multitechniques of neural, regression, classifier ML-DFT, Uniform Manifold Approximation Projection (UMAP). Nitrogen oxygen (N/O) graphene, graphene oxide (GO), N-doped GO were applied storage medium. Multi-ML algorithms employed CH4 uptake (i) N/O such pyridinic (N-py), carboxyl (O–II), oxidized (N-x), hydroxyl (O-h), Nitroso (N-ni), Amine (primary, secondary, tertiary). (ii) The surfaces are decorated with heteroatoms construct (GO) GO. DFT calculations by PW91 Dmol3 package. N/O-functionalities in distance ∼2.0 3.1 Å groups obtained Ead approximately −2.0 −4 eV. Further, ML models accomplished forthcoming physisorption using multiadsorptive features an R2 0.99. ML-derived sensitivity analysis approach was specifications deformation energy, functionality type, structure. indicate levels −0.03 0.02 synergetic DFT/ML approaches distinguished modeled rediscovered phases functional structures. UMAP is a new screening play complementary role modeling process.

Language: Английский

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Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 12

Published: Jan. 1, 2024

Promoting sustainable mining practices while safe-guarding water ecosystems demands precise anticipation of mine influx. This investigation pioneers a novel approach harnessing microseismic monitoring to detect water-conducting conduits and elevate proactive response strategies. Through the utilization energy density analysis, fracture points within rock formations are continuously monitored, offering real-time insights. Nonetheless, data generated from this method often exhibits fragmentation, sporadic patterns, heterogeneity, complicating identification evolving pathways. To surmount challenge, we have seamlessly integrated Self-Attention mechanism into Long Short-Term Memory (LSTM) model, resulting in innovative SA-LSTM fusion. hybrid model predicts following day's inflow, effectively merging with groundwater levels. fusion facilitates robust correlation between inflow metrics. Comparative assessments underscore SA-LSTM's superiority over other intricate time-series models terms forecast precision, MAE 21.8 m ³ /h, RMSE 39.3 /h MAPE 2.8% test stage event. By amalgamating diverse datasets, it substantially enhances accuracy predicting coal mines. The discernments study not only introduce more accurate predictions but also provide technical guidance for safety production mine.

Language: Английский

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Enhancing Fault Clearing Algorithm for Renewable Energy-based Distribution Systems Using Artificial Neural Networks

Clean Energy, Journal Year: 2024, Volume and Issue: 8(5), P. 97 - 116

Published: July 11, 2024

Abstract Integrating small and large-scale photovoltaic (PV) solar systems into electrical distribution has become mandatory due to increased electricity bills the concern for limiting greenhouse gases. However, reliable efficient operation of PV-based can be confronted by intermittence high variability sources their consequential faults. In this regard, article suggests a moderated fault-clearing strategy based on incremental conductance–maximum power point tracking (IC–MPPT) technique artificial neural networks (ANNs) enhance fault detection, localization, restoration processes in systems. The proposed leverages IC–MPPT ensure optimal generation from PV system, even presence By maximum point, algorithm maintains performance system mitigates against impact faults output power. Furthermore, an ANN is employed improve detection localization accuracy. developed ANN-based trained using historical data scenarios, enabling it recognize patterns make informed decisions through extensive simulations comparisons with traditional methods. To accomplish study, benchmarks are constructed MATLAB®/Simulink® software package. Moreover, validate efficacy strategy, real case study 1-MW industrial field located Giza governorate, Egypt, tested investigated. obtained results demonstrate effectiveness achieving faster precise solar-based while preserving extraction under large disturbances. achieves average 98.556 kW 299.632 kWh energy availability, whereas proportional–integral controller 95.7996 283.4036 kWh, classic perturb-and-observe MPPT 92.2657 276.8014 kWh.

Language: Английский

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Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

ACS Applied Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Language: Английский

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