Cited by Prediction of the higher heating values of biomass using machine learning methods based on proximate and ultimate analysis

Nanofluids application in machining: a comprehensive review DOI

Xiaoming Wang,

Yuxiang Song,

Changhe Li

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2023, Volume and Issue: 131(5-6), P. 3113 - 3164

Published: Jan. 4, 2023

Language: Английский

Citations

The stability and thermophysical properties of Al2O3-graphene oxide hybrid nanofluids for solar energy applications: Application of robust autoregressive modern machine learning technique DOI

Praveen Kumar Kanti, Prabhakar Sharma,

Manoor Prakash Maiya

et al.

Solar Energy Materials and Solar Cells, Journal Year: 2023, Volume and Issue: 253, P. 112207 - 112207

Published: Feb. 8, 2023

Language: Английский

Citations

Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio DOI

V. Vicki Wanatasanappan, Praveen Kumar Kanti, Prabhakar Sharma

et al.

Journal of Molecular Liquids, Journal Year: 2023, Volume and Issue: 375, P. 121365 - 121365

Published: Jan. 31, 2023

Language: Английский

Citations

Application of modern approaches to the synthesis of biohydrogen from organic waste DOI

Prabhakar Sharma, Akshay Jain, Bhaskor Jyoti Bora

et al.

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 48(55), P. 21189 - 21213

Published: April 5, 2023

Language: Английский

Citations

Thermal efficiency enhancement of mono and hybrid nanofluids in solar thermal applications – A review DOI

L. AlShuhail,

Feroz Shaik, L. Syam Sundar

et al.

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 68, P. 365 - 404

Published: Jan. 25, 2023

Everywhere throughout the world, primarily in all industrial sectors, there is a tremendous need for energy. The only option to meet energy demand via fossil fuels. Global warming and environmental pollution are caused by usage of fuels fast expansion industry. In addition, relying on renewable sources vital due finite availability most promising source world solar energy, which generally accessible surface globe. flat plate collector, can be transformed into thermal working fluid employed collector factor that influences its efficiency.The majority research found increasing collector's efficiency accomplished substituting high conductivity fluids called nanofluids hybrid fluid. A few decades ago, studies involving collectors were carried out. order significantly increase employing nanofluids, researchers currently these devices. By replacing one systems with fluid, larger effectiveness has been achieved. Nanofluids offer values over their base using provide augmented heat transfer coefficient, performance.Hybrid compared mono-dispersed nanofluids. Usage enhances properties including conductivity, density, viscosity specific heat. synthesis nanoparticles, characterization, thermophysical characteristics, stability analysis application mono systemsspecifically collectoris critically reviewed presented this paper.

Language: Английский

Citations

Twisted helical Tape's impact on heat transfer and friction in zinc oxide (ZnO) nanofluids for solar water heaters: Biomedical insight DOI

M. Arun,

Debabrata Barik,

Sreejesh S.R. Chandran

et al.

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 56, P. 104204 - 104204

Published: March 4, 2024

This study explores the influence of Zinc Oxide (ZnO) nanofluids on solar water heaters with Dimple Tubes and Helical Twisted Tape (DTHTT) surfaces. The helical twisted tape design enhances turbulence, improving nanofluid mixing thermal exchange. Computational fluid dynamics (CFD) validates efficiency parabolic trough heater (PTSWH) emphasis light concentration feed flow velocity. Optimal conditions include a 0.3% ZnO volume concentration, mass rates between 1.0 kg/min 5.0 kg/min, copper-type tapes. Experimental results reveal Nusselt number enhancements 15.1% 20.96% at H/D = 10, 16.72% 32.12% 3, for nanofluid, Reynolds numbers from 3000 to 8000. arrangement 3 exhibits increased mixing, leading higher convective heat transfer. Friction factor 8000 are 0.26% 0.38%, respectively, compared base fluid. At 3.0 rate, increases 39.25%, 13.25% gain over plain model shows ±3.24% deviation expected friction factor, total ±1.2% discrepancy experimental simulated findings, remaining within an acceptable range.

Language: Английский

Citations

Predicting the performance of a photovoltaic unit via machine learning methods in the existence of finned thermal storage unit DOI

Mohammadreza Kiaghadi,

M. Sheikholeslami,

A.M. Alinia

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 90, P. 111766 - 111766

Published: May 1, 2024

Language: Английский

Citations

Development of optimized machine learning models for predicting flat plate solar collectors thermal efficiency associated with Al2O3-water nanofluids DOI

Omer A. Alawi, Haslinda Mohamed Kamar, Sinan Q. Salih

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108158 - 108158

Published: March 8, 2024

Language: Английский

Citations

Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage DOI

Hung Vo Thanh, Hemeng Zhang, Zhenxue Dai

et al.

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 55, P. 1422 - 1433

Published: Dec. 21, 2023

Language: Английский

Citations

Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme DOI

Hung Vo Thanh, Mohammad Rahimi, Zhenxue Dai

et al.

Fuel, Journal Year: 2023, Volume and Issue: 345, P. 128183 - 128183

Published: March 24, 2023

Language: Английский

Citations