Novel application of support vector machines to model the two phase boiling heat transfer coefficient in a vertical tube thermosiphon reboiler

Zaidi, Sadaf

doi:10.1016/j.cherd.2015.04.002

Cited by 35 publications

(14 citation statements)

References 39 publications

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“…However, we expect the hyperplane with the larger margin to be more accurate in classifying new data than the hyperplane with the smaller margin. This is the reason that SVM searches for the hyperplane with the largest margin (Zaidi, 2015).…”

Section: Support Vector Machinementioning

confidence: 99%

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Adib

Kazerooni

Falsafi

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The acidic compounds such as Mercaptans, H2S and COS are commonly present in the liquid LPG streams in the south Pars gas processing plant. Sulfur contaminants not only lead to odor problems but can form objectionable oxides on combustion and cause environmental pollution. In present study, Support Vector Machine (SVM) is employed to develop an intelligent model to predict the sulfur content of propane and butane products of Liquefied Petroleum Gas (LPG) treatment unit of south Pars gas processing plant of Assaluyeh/Iran. A set of seven input/output plant data each consisting of 365 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 70%, 15%, and 15% of available data respectively. Test results from the SVM developed model showed good compliance with operating plant data. Squared correlation coefficients for developed models are 0.97 and 0.99 for propane and butane sulfur content, respectively. According to the results of the present case study, SVM could be regarded as a reliable accurate approach for modeling the sulfur content of LPG treatment unit of a natural gas processing plant.

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Section: Support Vector Machinementioning

confidence: 99%

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Adib

Kazerooni

Falsafi

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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show abstract

“…The whole dataset is grouped into a dependent parameter (output/target) and independent input parameters for SVR modeling and then divided into two groups of 80% the total data (255 data points) and 20% the total data (64 data points) to create the training dataset and the test dataset, respectively. Among the various kernel functions-such as linear kernel, polynomial kernel, radial basis function (RBF) kernel, and sigmoid-the RBF kernel is chosen for its good general performance and only needing one parameter to be set [12,29]. Table 3 gives the optimal measures of the SVR hyper-parameters C, ε, and the RBF kernel (γ) using the exhaustive grid search technique, which has 10-fold cross-validation.…”

Section: Development Of An Svr-based Modelmentioning

confidence: 99%

“…In open literature, SVR has many applications for the prediction of many real-world problems, such as permeability predictions for hydrocarbon reservoirs [9], wind speed forecasting for wind farms [10], predicting for carbon monoxide in the atmosphere [11], predicting the heat transfer coefficient in a thermosiphon reboiler [12], and predicting heavy metal removal efficiency [13,14]. In the current study, the prediction of the heat transfer coefficient of the refrigerant R600a is made by using two artificial intelligence techniques (AI) namely, SVR and artificial neural networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Development and Analyses of Artificial Intelligence (AI)-Based Models for the Flow Boiling Heat Transfer Coefficient of R600a in a Mini-Channel

Parveen¹,

Zaidi²,

Danish³

2018

ChemEngineering

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Environmental friendly refrigerants with zero ozone depletion potential (ODP) and zero global warming potential (GWP) are in great demand across the globe. One such popular refrigerant is isobutane (R600a) which, having zero ODP and negligible GWP, is considered in this study. This paper presents the two most popular artificial intelligence (AI) techniques, namely support vector regression (SVR) and artificial neural networks (ANN), to predict the heat transfer coefficient of refrigerant R600a. The independent input parameters of the models include mass flux, saturation temperature, heat flux, and vapor fraction. The heat transfer coefficient of R600a is the dependent output parameter. The prediction performance of these AI-based models is compared and validated against the experimental results, as well as with the existing correlations based on the statistical parameters. The SVR model based on the structural risk minimization (SRM) principle is observed to be superior compared with the other models and is more accurate, precise, and highly generalized; it has the lowest average absolute relative error (AARE) at 1.15% and the highest coefficient of determination (R 2) at 0.9981. ANN gives an AARE of 5.14% and a R 2 value of 0.9685. Furthermore, the simulated results accurately predict the effect of input parameters on the heat transfer coefficient.

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“…They found mean square error (MSE) as 4.17 in predictive model. Zaidi [11] predicted the two phase boiling heat transfer coefficient using SVM. He found predictive value of RMSE as 0.581.…”

Section: Mr -Drying Timementioning

confidence: 99%

“…Artificial neural networks are usually used to construct these models [7][8][9][10]. Predictive models of h c values were created by using SVM regression in different topics [11,12]. The use of SVM regression for predicting h c values in food drying systems has not been found in literature.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

Daş

Akpınar

2018

Applied Sciences

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Abstract:In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (h c ) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of h c in both drying systems with a range 12.4 and 20.8 W/m 2 • C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated h c values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model's accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated h c values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the h c values.

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Novel application of support vector machines to model the two phase boiling heat transfer coefficient in a vertical tube thermosiphon reboiler

Cited by 35 publications

References 39 publications

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Development and Analyses of Artificial Intelligence (AI)-Based Models for the Flow Boiling Heat Transfer Coefficient of R600a in a Mini-Channel

Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

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