Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Energy & Fuels, Journal Year: 2022, Volume and Issue: 36(13), P. 6626 - 6658

Published: June 13, 2022

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity working fluid has a huge impact on efficiency system. addition small amount high thermal conductivity solid nanoparticles to base improves transfer. Even though large research data is available literature, some results are contradictory. Many influencing factors, as well nonlinearity refutations, make nanofluid highly challenging obstruct its potentially valuable uses. On other hand, data-driven machine learning techniques would be very useful for forecasting thermophysical features rate, identifying most influential assessing efficiencies different primary aim this review study look at applications employed nanofluid-based system, reveal new developments research. A variety modern algorithms studies systems examined, along with their advantages disadvantages. Artificial neural networks-based model prediction using contemporary commercial software simple develop popular. prognostic may further improved by combining marine predator algorithm, genetic swarm intelligence optimization, intelligent optimization approaches. In well-known networks fuzzy- gene-based techniques, newer ensemble such Boosted regression K-means, K-nearest neighbor (KNN), CatBoost, XGBoost gaining due architectures adaptabilities diverse types. regularly used fuzzy-based mostly black-box methods, user having little or no understanding how they function. This reason concern, ethical artificial required.

Language: Английский

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Synthesis, stability, density, viscosity of ethylene glycol-based ternary hybrid nanofluids: Experimental investigations and model -prediction using modern machine learning techniques

Powder Technology, Journal Year: 2022, Volume and Issue: 400, P. 117190 - 117190

Published: Feb. 12, 2022

Language: Английский

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Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale Organic Rankine Cycle (ORC) using hybrid nanofluid

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 360, P. 132194 - 132194

Published: May 12, 2022

Language: Английский

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A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries

Batteries, Journal Year: 2022, Volume and Issue: 9(1), P. 13 - 13

Published: Dec. 25, 2022

The intense increase in air pollution caused by vehicular emissions is one of the main causes changing weather patterns and deteriorating health conditions. Furthermore, renewable energy sources, such as solar, wind, biofuels, suffer from supply chain-related uncertainties. electric vehicles’ powered energy, stored a battery, offers an attractive option to overcome uncertainties certain extent. development implementation cutting-edge vehicles (EVs) with long driving ranges, safety, higher reliability have been identified critical decarbonizing transportation sector. Nonetheless, capacity time usage, environmental degradation factors, end-of-life repurposing pose significant challenges usage lithium-ion batteries. In this aspect, determining battery’s remaining usable life (RUL) establishes its efficacy. It also aids testing various EV upgrades identifying factors that will improve their efficiency. Several nonlinear complicated parameters are involved process. Machine learning (ML) methodologies proven be promising tool for optimizing modeling engineering domain (non-linearity complexity). contrast scalability temporal limits battery degeneration, ML techniques provide non-invasive solution excellent accuracy minimal processing. Based on recent research, study presents objective comprehensive evaluation these challenges. RUL estimations explained detail, including examples approach applicability. many thoroughly individually studied. Finally, application-focused overview offered, emphasizing advantages terms efficiency accuracy.

Language: Английский

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Performance optimization of energy-efficient solar absorbers for thermal energy harvesting in modern industrial environments using a solar deep learning model

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26371 - e26371

Published: Feb. 1, 2024

Thermal energy harvesting has seen a rise in popularity recent years due to its potential generate renewable from the sun. One of key components this process is solar absorber, which responsible for converting radiation into thermal energy. In paper, smart performance optimization efficient absorber proposed modern industrial environments using deep learning model. model, data collected multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and radiation. This then used train machine algorithm make predictions on how much can be harvested particular panel or system. computational range, model (SDLM) reached 83.22 % testing 91.72 training results false positive absorption rate, 69.88 81.48 discovery 81.40 72.08 omission 75.04 73.19 absorbance prevalence threshold, 90.81 78.09 critical success index. The also incorporates insulation orientation further improve accuracy predicting amount harvested. Solar absorbers are absorb sun's turn it power things heating cooling systems, air compressors, even some types manufacturing operations. By businesses accurately predict before making an investment

Language: Английский

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Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 150059 - 150059

Published: Feb. 28, 2024

Language: Английский

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Experimental analysis of novel ionic liquid-MXene hybrid nanofluid's energy storage properties: Model-prediction using modern ensemble machine learning methods

Journal of Energy Storage, Journal Year: 2022, Volume and Issue: 52, P. 104858 - 104858

Published: May 26, 2022

Language: Английский

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Precise prediction of performance and emission of a waste derived Biogas–Biodiesel powered Dual–Fuel engine using modern ensemble Boosted regression Tree: A critique to Artificial neural network

Fuel, Journal Year: 2022, Volume and Issue: 321, P. 124131 - 124131

Published: April 9, 2022

Language: Английский

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Improving the thermal efficiency of a solar flat plate collector using MWCNT-Fe3O4/water hybrid nanofluids and ensemble machine learning

Case Studies in Thermal Engineering, Journal Year: 2022, Volume and Issue: 40, P. 102448 - 102448

Published: Sept. 24, 2022

The thermal performance of a flat plate solar collector using MWCNT + Fe3O4/Water hybrid nanofluids was examined in this research. tested different nanofluid concentrations and flow rates an arid environment. A significant enhancement coefficient heat transfer (26.3%) with marginal loss on pressure drop due to friction factor (18.9%). data collected during experimental testing utilized develop novel prediction models for efficient transfer, Nusselt's number, factor, efficiency. modern ensemble machine learning techniques Boosted Regression Tree (BRT) Extreme Gradient Boosting (XGBoost) were used prognostic each parameter. battery statistical methods Taylor's graphs compare the these two ML techniques. value R2 BRT-based 0.9619 - 0.9994 0.9914 0.9997 XGBoost-based models. mean squared error quite low all (0.000081 9.11), while absolute percentage negligible from 0.0025 0.3114. comprehensive analysis model complemented improved comparison paradigm, reveal superiority XGBoost over BRT.

Language: Английский

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Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 607, P. 127549 - 127549

Published: Feb. 5, 2022

Language: Английский

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