Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks
Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leve...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024011137 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846115899062353920 |
|---|---|
| author | Dheyaa J. jasim Husam Rajab As'ad Alizadeh Kamal Sharma Mohsen Ahmed Murizah Kassim S. AbdulAmeer Adil A. Alwan Soheil Salahshour Hamid Maleki |
| author_facet | Dheyaa J. jasim Husam Rajab As'ad Alizadeh Kamal Sharma Mohsen Ahmed Murizah Kassim S. AbdulAmeer Adil A. Alwan Soheil Salahshour Hamid Maleki |
| author_sort | Dheyaa J. jasim |
| collection | DOAJ |
| description | Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions—the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)—demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency. |
| format | Article |
| id | doaj-art-a18984dd6a1a49d98b1cc15c1670db42 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-a18984dd6a1a49d98b1cc15c1670db422024-12-19T10:57:07ZengElsevierResults in Engineering2590-12302024-12-0124102858Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networksDheyaa J. jasim0Husam Rajab1As'ad Alizadeh2Kamal Sharma3Mohsen Ahmed4Murizah Kassim5S. AbdulAmeer6Adil A. Alwan7Soheil Salahshour8Hamid Maleki9Department of Petroleum Engineering, Al-Amarah University College, Maysan, IraqCollege of Engineering, Mechanical Engineering Department, Alasala University, King Fahad Bin Abdulaziz Rd., P.O.Box: 12666, Amanah, 31483, Dammam, Kingdom of Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, IraqInstitute of Engineering and Technology, GLA University, Mathura, U.P. 281406, IndiaImam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Eastern Province, Kingdom of Saudi ArabiaInstitute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia; School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, MalaysiaDepartment of Automobile Engineering, College of Engineering, Al-Musayab, University of Babylon, Iraq; Ahl Al Bayt University, Kerbala, IraqCollege of Technical Engineering, National University of Science and Technology, Dhi Qar, 64001, IraqFaculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey; Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonRenewable Energy Research Group, Isfahan, Iran; Corresponding author.Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions—the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)—demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency.http://www.sciencedirect.com/science/article/pii/S2590123024011137Solar energy conversionPVT solar panelsArtificial neural networkMachine learningMXeneGraphene |
| spellingShingle | Dheyaa J. jasim Husam Rajab As'ad Alizadeh Kamal Sharma Mohsen Ahmed Murizah Kassim S. AbdulAmeer Adil A. Alwan Soheil Salahshour Hamid Maleki Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks Results in Engineering Solar energy conversion PVT solar panels Artificial neural network Machine learning MXene Graphene |
| title | Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks |
| title_full | Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks |
| title_fullStr | Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks |
| title_full_unstemmed | Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks |
| title_short | Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks |
| title_sort | enhancing solar energy conversion efficiency thermophysical property predicting of mxene graphene hybrid nanofluids via bayesian optimized artificial neural networks |
| topic | Solar energy conversion PVT solar panels Artificial neural network Machine learning MXene Graphene |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024011137 |
| work_keys_str_mv | AT dheyaajjasim enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT husamrajab enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT asadalizadeh enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT kamalsharma enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT mohsenahmed enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT murizahkassim enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT sabdulameer enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT adilaalwan enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT soheilsalahshour enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks AT hamidmaleki enhancingsolarenergyconversionefficiencythermophysicalpropertypredictingofmxenegraphenehybridnanofluidsviabayesianoptimizedartificialneuralnetworks |