A novel approach for optimizing a photovoltaic thermal system combined with solar thermal collector: Integrating RSM, multi-objective bat algorithm and VIKOR decision maker DOI
Chou‐Yi Hsu, Harikumar Pallathadka,

Pinank Patel

et al.

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2024, Volume and Issue: 168, P. 105927 - 105927

Published: Dec. 30, 2024

Language: Английский

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy DOI Creative Commons
Tao Hai, Ali Basem,

As’ad Alizadeh

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 15, 2025

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary nanofluids. goal minimize dynamic viscosity and maximize thermal conductivity varying volume fraction, temperature, nanomaterial mixing ratio. proposed integrates machine learning, multi-objective optimization, multi-criteria decision-making. Three learning techniques—GMDH-type neural network, gene expression programming, combinatorial algorithm—are applied model as functions input variables. Then, high-performing models provide foundation optimization using well-established particle swarm algorithm. Finally, decision-making technique TOPSIS employed identify most desirable points from Pareto front, various design scenarios. To validate strategy, composed graphene oxide (GO), iron (Fe₃O₄), titanium dioxide (TiO₂) was case study. results demonstrated that approach excelled in accurately modeling (R = 0.99964–0.99993). process revealed VFs span broad range across all ratios, while temperatures were consistently near maximum value (65 °C). outcomes indicated ratio consistent scenarios, with fraction serving key differentiating factor.

Language: Английский

Citations

4
Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models DOI
Yi Ru,

Ali B.M. Ali,

Karwan Hussein Qader

et al.

International Journal of Thermal Sciences, Journal Year: 2025, Volume and Issue: 211, P. 109691 - 109691

Published: Jan. 13, 2025

Language: Английский

Citations

1
Synergizing Neural Networks with Multi-Objective Thermal Exchange Optimization and PROMETHEE Decision-Making to Improve PCM-based Photovoltaic Thermal Systems DOI Creative Commons

Li Yongxin,

Ali Basem,

As’ad Alizadeh

et al.

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105851 - 105851

Published: Feb. 1, 2025

Language: Английский

Citations

0
Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems DOI Creative Commons
Ali Basem,

Hanaa Kadhim Abdulaali,

As’ad Alizadeh

et al.

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: unknown, P. 100835 - 100835

Published: Dec. 1, 2024

Language: Английский

Citations

2
A novel approach for optimizing a photovoltaic thermal system combined with solar thermal collector: Integrating RSM, multi-objective bat algorithm and VIKOR decision maker DOI
Chou‐Yi Hsu, Harikumar Pallathadka,

Pinank Patel

et al.

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2024, Volume and Issue: 168, P. 105927 - 105927

Published: Dec. 30, 2024

Language: Английский

Citations

2