Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in Optimization of Aegle marmelos Oil Extraction for Biodiesel Production

Selvan, S. Sindhanai; Pandian, P. Saravana; Subathira, A.; Saravanan, S.

doi:10.1007/s13369-018-3272-5

Cited by 32 publications

(14 citation statements)

References 35 publications

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“…These findings confirm that the model ANN is better than the RSM model. SEP and AAD check the significance and accuracy of the models [21]. The low values of the mentioned statistical parameters indicates the better the performance of the predicted model.…”

Section: The Predictive Capability Of Modelsmentioning

confidence: 99%

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

S*¹,

Anbessa²

2019

IJEAT

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In the present investigation, the transesterification of waste cooking oil (WCO) to biodiesel over homogenous catalyst KOH have been carried out. To optimize the transesterification process variables both response surface method (RSM) and artificial neural network (ANN) mathematical models were applied to study the impact of process variables temperature, catalyst loading, methanol to oil ratio and the reaction time on biodiesel yield. The experiments were planned with a central composite design matrix using 24 factorial designs. A performance validation assessment was conducted between RSM and ANN. ANN models showed a high precision prediction competence in terms of coefficient of determination (R2 = 0.9995), Root Mean Square Error (RMSE = 0.5702), Standard Predicted Deviation (SEP = 0.0133), Absolute Average Deviation (AAD = 0.0115) compared to RSM model. The concentration of catalyst load was identified as the most significant factor for the base catalyzed transesterification. Under optimum conditions, the maximum biodiesel yield of 88.3% was determined by the artificial neural network model at 60 ºC, 1.05 g catalyst load, 7:1 methanol to oil ratio and 90 min transesterification reaction time. The biodiesel was analyzed by GCMS and it showed the presence of hexadecanoic acid, 9- octadecenoic acid, 9, 12, 15-octadecatrienoic acid, eicosenoic acid, methyl 18-methyl-nonadecanoate, docosanoic acid, and tetracosanoic acid as key fatty acid methyl esters.

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Section: The Predictive Capability Of Modelsmentioning

confidence: 99%

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

S*¹,

Anbessa²

2019

IJEAT

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“…Despite its simplicity and efficiency, RSM provides efficient and accurate solutions. Therefore, it has successfully been applied in many engineering problems [11] [13].…”

Section: Modelingmentioning

confidence: 99%

“…It can handle obscure, complex, incomplete problem and execute modeling to produce predictions and generalizations at high speed. Both RSM and ANN techniques do not need the precise expressions or the physical meaning of the system under investigation [13].…”

Section: Modelingmentioning

confidence: 99%

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

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The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

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“…For MIT BIH arrhythmia database, the ECG feature space The neural network is made of stimulated biological like small units called neurons which are interlinked to perform complex tasks [24]. NN or artificial neural network (ANN) has an extensive spectrum for applications in pattern recognition, classification, optimization, reasoning, approximation [25]. They are showing remarkable results in medical diagnosis, data mining face recognition, fault detection, etc.…”

Section: Distance Measuresmentioning

confidence: 99%

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

Singh

Rajpal

Mehta

2018

ICST Transactions on Scalable Information Systems

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Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P-QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.

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Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in Optimization of Aegle marmelos Oil Extraction for Biodiesel Production

Cited by 32 publications

References 35 publications

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

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