Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach

Miraboutalebi, Seyed Mohammadreza; Kazemi, Pezhman; Bahrami, Peyman

doi:10.1016/j.fuel.2015.10.118

Cited by 88 publications

(32 citation statements)

References 26 publications

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“…The standard analysis of the cetane number as a parameter to evaluate the biodiesel quality it is a relatively complex and expensive technique (Lapuerta et al, 2009;Knothe, 2014). Basically, this indicates aspects related to the ignition characteristics that can impact the engine performance, noise and exhaust emission levels (Tat, 2011;Knothe, 2014;Miraboutalebi et al, 2016). As the cetane number depends also of the fatty acids content in the oil, different authors have developed empirical models to correlate and predict the cetane number based on the composition and physicochemical properties of biodiesel (Lapuerta et al, 2009;Ramírez-Verduzco et al, 2012;Nadai et al, 2013;Miraboutalebi et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Basically, this indicates aspects related to the ignition characteristics that can impact the engine performance, noise and exhaust emission levels (Tat, 2011;Knothe, 2014;Miraboutalebi et al, 2016). As the cetane number depends also of the fatty acids content in the oil, different authors have developed empirical models to correlate and predict the cetane number based on the composition and physicochemical properties of biodiesel (Lapuerta et al, 2009;Ramírez-Verduzco et al, 2012;Nadai et al, 2013;Miraboutalebi et al, 2016). Particularly in this work, the method reported by Nadai et al (2013) was used which allows estimating the cetane number on the basis of the fatty acid composition and data from 1 H NMR spectrum for biodiesel.…”

Section: Resultsmentioning

confidence: 99%

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Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Silveira¹,

Simionatto

Pérez³

et al. 2016

Industrial Crops and Products

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Silveira¹,

Simionatto

Pérez³

et al. 2016

Industrial Crops and Products

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“…Intelligent methods include the wide spectrum of soft computing methods from fuzzy logic to artificial neural networks (ANNs) that are used for prediction, classification, robust, and non‐linear control techniques . In recent years there were developed various studies based on intelligent methods to predict the CN value . In a study by Ramadhas et al , it was used ANN to predict CN of biodiesel (sunflower methyl ester).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Ardabili

Najafi

Shamshirband³

2019

Env Prog and Sustain Energy

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The aim of present study is to develop an accessible accurate estimation of CN based on fatty acid methyl esters and to provide a proper solution for presenting a user‐friendly method. In fact, this study calculates the density, viscosity, and HHV based on FAMEs and predicts the CN by employing fuzzy method within a set. This is an interesting approach that has not been used in similar articles. Gaussian membership functions with 81 roles were employed to develop the fuzzy model. The model was developed based on Carbon number, Double bond, Saponification number, and Iodine value to predict the CN. Performance factors of r, RMSE, MAE, and R2 were calculated as 0.9912, 1.0723, 0.63427, and 0.9828, respectively to predict the CN. The results of FAMEs effect on properties of biodiesel showed, increasing Carbon number of FAMEs increases the CN, viscosity, and HHV, but increasing the number of Double bonds decreases CN, viscosity, and HHV. While the effect of increasing Carbon number of FAMEs on density was vice versa. Based on results, C16:00, C18:00, C18:1, and C18:02 FAMEs are approximately in components of all conventional oils; therefore, they can be effective on physical properties of biodiesels. © 2019 American Institute of Chemical Engineers Environ Prog, 38: 584–599, 2019

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“…The tree construction algorithm uses recursive partitioning approach to split the larger space into two smaller pieces. The selection of split point is an optimization problem based on the squared error loss 25,26. “randomForest” package in an R environment was used to develop RF models 15,27.…”

Section: Methodsmentioning

confidence: 99%

Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

Kazemi¹,

Khalid²,

Gago³

et al. 2017

DDDT

Self Cite

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Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

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Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach

Cited by 88 publications

References 26 publications

Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

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Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach

Cited by 88 publications

References 26 publications

Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Potential of Virginia-type peanut (Arachis hypogaea L.) as feedstock for biodiesel production

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Effect of roll compaction on granule size distribution of microcrystalline cellulose&ndash;mannitol mixtures: computational intelligence modeling and parametric analysis

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Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis