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

Solar Energy Materials and Solar Cells, Год журнала: 2023, Номер 253, С. 112207 - 112207

Опубликована: Фев. 8, 2023

Язык: Английский

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The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: Application of novel machine learning technique

Journal of Energy Chemistry, Год журнала: 2023, Номер 82, С. 359 - 374

Опубликована: Апрель 10, 2023

Язык: Английский

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Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Energy & Fuels, Год журнала: 2024, Номер 38(3), С. 1692 - 1712

Опубликована: Янв. 19, 2024

Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization ML the development scaling up systems needs a high degree accountability. However, most approaches currently use termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) an attractive option solve issue poor interoperability black-box methods. This review investigates relationship between (RE) XAI. It emphasizes potential advantages XAI improving performance efficacy RE systems. realized that although integration with has enormous alter how produced consumed, possible hazards barriers remain be overcome, particularly concerning transparency, accountability, fairness. Thus, extensive research required address societal ethical implications using create standardized data sets evaluation metrics. In summary, this paper shows potential, perspectives, opportunities, challenges application system management operation aiming target efficient energy-use goals more sustainable trustworthy future.

Язык: Английский

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Machine learning and multi-criteria decision analysis for polyethylene air-gasification considering energy and environmental aspects

Process Safety and Environmental Protection, Год журнала: 2024, Номер 183, С. 46 - 58

Опубликована: Янв. 5, 2024

Язык: Английский

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Prediction of jet impingement solar thermal air collector thermohydraulic performance using soft computing techniques

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 55, С. 104144 - 104144

Опубликована: Фев. 22, 2024

Solar thermal air collectors are economically efficient for the purpose of heating in specific applications. This research examines efficiency a solar collector called jet impingement (JISTAC) that is fitted with discrete multi-arc-shaped ribs (DMASRs) utilizing soft computing techniques. Linear Regression (LR), M5P, Gaussian Process (GP), and Random Forest (RF) used to predict Nusselt number (Nu), friction factor (f), thermohydraulic performance (ηthp) JISTAC. DMASRs absorber plates different relative rib height (0.025–0.047), width (0.32–1.72), arc angle (35°–65°), distance (0.27–0.86), pitch (0.58–3.1) were used. The tests yielded 245 data sets, 173 assigned training 72 testing. GP surpasses other models owing its minimum error maximum correlation coefficient value. model has MAE 0.0347, RMSE 0.0564, RAE 10.88%, RRSE 12.77%, CC value 0.9920. Furthermore, it 99%, making most prominent among models. results indicate quite precise predicting ηthp

Язык: Английский

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A new strategy for the preparation of multi-walled carbon nanotubes/NiMoO4 nanostructures for high-performance asymmetric supercapacitors

Journal of Energy Storage, Год журнала: 2022, Номер 59, С. 106438 - 106438

Опубликована: Дек. 27, 2022

Язык: Английский

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Application of carbon nanotube prepared from waste plastic to phase change materials: The potential for battery thermal management

Waste Management, Год журнала: 2022, Номер 154, С. 96 - 104

Опубликована: Окт. 10, 2022

Carbon nanotube (CNT), has been demonstrated as a promising high-value product from thermal chemical conversion of waste plastics and securing new applications is an important prerequisite for large-scale production CNT waste-plastic recycling. In this study, CNT, produced plastic through vapor deposition (pCNT), was applied nanofiller in phase change material (PCM), affording pCNT-PCM composites. Compared with pure PCM, the addition 5.0 wt% pCNT rendered peak melting temperature increase by 1.3 ℃, latent heat retain 90.7%, conductivity 104%. The results morphological analysis leakage testing confirmed that similar PCM encapsulation performance shape stability to those commercial CNT. formation uniform cluster networks allowed large loading into on premise free change, responsible high inside homogeneous phase. Thus, resulting capillary forces retained capacity suitable prohibited matrix outside during re-melting ratio increased. Therefore, as-prepared composite believed have potential cCNT shows prominent flowable conductive filler battery management systems.

Язык: Английский

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Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Дек. 21, 2022

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in presence ternary-hybrid nanomaterials cerium oxide (CeO2), graphene (GO), silica aerogel (SA) were investigated experimentally. Nanofluid was measured over a volume fraction range VF = 0.25-1.5%, temperature T 5-55 °C, shear rate SR 40-1000 rpm. The preparation nanofluids involved two-step process, dispersed SAE using magnetic stirrer ultrasonic device. addition, CeO2, GO, SA nanoadditives underwent X-ray diffraction-based structural analysis. non-Newtonian (pseudoplastic) nanofluid at all temperatures fractions is revealed by analyzing stress, viscosity, power-law model coefficients. However, tend to Newtonian low temperatures. For instance, declines with increasing between 4.51% (at 5 °C) 41.59% 55 for 1.5 vol% nanofluid. experimental results demonstrated that decreasing fraction. assuming constant 100 rpm increase from increases least 95.05% (base fluid) no more than 95.82% (1.5 nanofluid). Furthermore, 0 1.5%, minimum 14.74% maximum 35.94% °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, RSM) used develop models nanofluid's their accuracy complexity compared. COMBI algorithm R2 0.9995 had highest among developed models. Additionally, RSM able generate predictive complexity.

Язык: Английский

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Deep learning algorithms were used to generate photovoltaic renewable energy in saline water analysis via an oxidation process

Journal of Water Reuse and Desalination, Год журнала: 2023, Номер unknown

Опубликована: Фев. 1, 2023

Abstract The amount of particles and organic matter in wash-waters effluent from the processing fruits vegetables determines whether they need to be treated fulfil regulatory standards for their intended use. This research proposes a novel technique photovoltaic cell-based renewable energy saline water analysis using oxidation process deep learning techniques. Here, is carried out based on Markov fuzzy-based Q-radial function neural networks (MFQRFNN). plan entirely web-oriented enable better control effective monitoring consumption. makes use communication system that collects data form irregularly spaced time series. Experimental has been salinity terms accuracy, precision, recall, specificity, computational cost, kappa coefficient.

Язык: Английский

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A critical review on renewable battery thermal management system using heat pipes

Journal of Thermal Analysis and Calorimetry, Год журнала: 2023, Номер 148(16), С. 8403 - 8442

Опубликована: Май 2, 2023

Язык: Английский

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