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AkademScholar

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Ishlab chiquvchilar uchun

AkademBasetez oradaDasturchilar API
Mualliflar

Fahad M. Alhomayani

Soʻnggi maʼlum muassasa:
Taif University
ORCID:
0000-0002-4914-8722
5
h-indeks
0
i10-indeksi
54
Iqtiboslar
21
Asarlar
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0510152001325385020232024202520262023: Nashrlar 1, Iqtiboslar 02024: Nashrlar 11, Iqtiboslar 72025: Nashrlar 9, Iqtiboslar 352026: Nashrlar 0, Iqtiboslar 12

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Eng koʻp iqtibos qilingan asarlar

  1. Heat Re-process approach and thermally integrated renewable energy system for power, compressed hydrogen, and freshwater production; ANN boosted optimization and techno-enviro-economic analysis20256 iqtibos
  2. A geothermal-based freshwater/cooling system assisted by heat recovery sections: 3E analysis and techno-economic optimization using genetic algorithm20246 iqtibos
  3. Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques20255 iqtibos
  4. Advancing solar thermal utilization by optimization of phase change material thermal storage systems: A hybrid approach of artificial neural network (ANN)/Genetic algorithm (GA)20244 iqtibos
  5. Efficient thermal integration model based on a biogas-fired gas turbine cycle (GTC) for electricity and desalination applications; thermo-economic and GA-based optimization20244 iqtibos
  6. Optimizing and investigating the charging time of phase change materials in a compact-latent heat storage using pareto front analysis, artificial neural networks, and numerical simulations20244 iqtibos
  7. Heat energy utilization of a double-flash geothermal source efficiently for heating/electricity supply through particle swarm optimization method20243 iqtibos
  8. Harnessing the power of waste in a poly-output system transforming biomass feedstocks into sustainable Bio-H2, O2, electricity, and heating20241 iqtibos
  9. Artificial intelligence-powered study of a waste-to-energy system through optimization by regression-centered machine learning algorithms20251 iqtibos
  10. Improving energy absorption capacity in a thermal energy storage system for domestic heating applications using an integrated machine learning/numerical approach20251 iqtibos