Prediction of the Dynamic Viscosity of MXene/palm Oil Nanofluid Using Support Vector Regression

Parashar, Naman; Khan, Jawad; Aslfattahi, Navid; Saidur, R.; Yahya, Syed Mohd

doi:10.1007/978-981-16-3428-4_5

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…adjusted ε can improve the SVR accuracy [41][42][43]. In our results, the ε was also found to influence the SVR accuracy in hyperparameters of both SVR and FSVR algorithms.…”

Section: Conflicts Of Interestsupporting

confidence: 56%

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

et al. 2022

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As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all <2.5%), mean absolute error, and root mean square error for all types of traffic at all the analyzed airports. Fuzzy sets can handle uncertainty and imprecision in time series. Therefore, the prediction accuracy of the entire time series model is improved by taking advantage of SVR and fuzzy sets. By using the highly accurate FSVR model to predict the future growth of air freight volume, airport management could analyze their existing facilities and service capacity to identify operational bottlenecks and plan future development. The FSVR model is the most accurate forecasting model for air traffic forecasting.

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“…adjusted ε can improve the SVR accuracy [41][42][43]. In our results, the ε was also found to influence the SVR accuracy in hyperparameters of both SVR and FSVR algorithms.…”

Section: Conflicts Of Interestsupporting

confidence: 56%

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

et al. 2022

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“…A chart containing the steps followed to achieve this is shown in Figure 1. The chart illustrates the dependency of the present work on experimental measurements of the properties of the PO/MXene nanofluid, conducted through our group at an earlier time [9,13,22,23].…”

Section: Methodsmentioning

confidence: 99%

Insights on the thermal potential of a state-of-the-art palm oil/MXene nanofluid in a circular pipe

Abdelrazik

Saidur²,

Al‐Sulaiman³

2022

J Therm Anal Calorim

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MXene, a recently created nanomaterial, offers significant potential for thermal, electrical, and a variety of other uses. MXene was utilized to generate heat transfer nanofluids with improved thermophysical properties for thermal applications and to establish the optimal parameters for achieving the best thermal performance. In this study, a palm oil/MXene nanofluid was used as the heat transfer fluid in a circular pipe to evaluate its thermal impact at different Reynolds numbers and applied heat fluxes at a range of introduced MXene nanoparticles' concentrations. Thermal conductivity and viscosity were shown to be linked to temperature and nanoparticle concentrations ranging from 0.01 to 0.1 mass%. The influence of concerted MXene nanoparticles (0.01 to 0.1 mass%) on the behavior of the PO/MXene nanofluid, was studied using CFD simulations at various flow Reynolds numbers (2,500 to 5,000) and wall heat fluxes (40,000 to 90,000 W.m -2 ). The results indicate that increasing the nanoparticle concentration resulted in higher heat transfer coefficients and lower Nusselt numbers. MXene nanoparticles were more efficient at lowering the wall temperature and increasing the pace of cooling when applied at larger heat fluxes and lower Re numbers.The results reported in this article indicate that MXene nanomaterials have a strong potential for overcoming the low heat transfer difficulties encountered in heat exchange systems.

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“…The third most preferred algorithm for prediction is SVM, though several flaws must be resolved. The SVM results were more significant than the MSC-PCA hybrid with SVM, SVM-RBF, and SVM-ANN [104,107]. This result implies that the increased SVM performance is attributed to the improved optimisation techniques for a wide range of parameters [114].…”

Section: Analysis and Discussionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Prediction of the Dynamic Viscosity of MXene/palm Oil Nanofluid Using Support Vector Regression

Cited by 7 publications

References 11 publications

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

Insights on the thermal potential of a state-of-the-art palm oil/MXene nanofluid in a circular pipe

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

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