scholarly journals Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

2016 ◽  
Vol 3 (1) ◽  
pp. 19-25
Author(s):  
T. Petkov ◽  
Z. Mustafa ◽  
S. Sotirov ◽  
R. Milina ◽  
M. Moskovkina

Abstract A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kwang Seo Park ◽  
Yun Ju Kim ◽  
Eun Kyung Choe

To implement EU REACH- (Registration, Evaluation, and Authorization of Chemicals-) like chemical legislations in various countries of which the purpose is human and environment safety, the first step is substance identification followed by the hazard and risk assessments. Although both structural and composition identifications are required, the latter can more importantly result in the essential data to fill out the required substance information such as purity and concentrations of constituents, as well as impurities. With fatty acid zinc salts (FAZSs) as an exemplary industrial chemical of which chromatographic and nuclear magnetic resonance (NMR) analyses were impossible due to their insolubility in water and any organic solvents, the composition characterization was tried by preparing their fatty acid methyl esters (FAMEs) using the conc. HCl/methanol/toluene method. This acid-catalyzed methyl esterification was optimized with zinc stearate as a surrogate substance. Gas chromatography-mass spectrometry (GC-MS) and NMR analyses on methyl-esterified products revealed that the optimum conditions were at 90°C for 10 min or 45°C for 30 min with two equivalent HCl as well as at 45°C for 10 min with five equivalent HCl. Almost all zinc stearates were converted into the corresponding fatty acids with 97–99% conversion rates. Free fatty acids (FFAs) were detected in extracted ion chromatograms of pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) in the methyl-esterified products with incomplete conversions of 73∼79%. The optimized conc. HCl/methanol/toluene method of direct one-step reaction from FAZSs was compared with the two-step NaOH saponification/BF3-methanol method after acidic hydrolysis of FAZSs. The mechanism of fatty acid zinc salts into free fatty acids and fatty acid methyl esters was suggested with the evidence of the formation of Zn(OH)2.


2010 ◽  
Vol 88 (9) ◽  
pp. 898-905 ◽  
Author(s):  
Liyan Liu ◽  
Ying Li ◽  
Rennan Feng ◽  
Changhao Sun

A method for simultaneous determination of 16 free fatty acids (FFAs) in serum is described. The method involves conversion of FFAs to fatty acid methyl esters (FAMEs) using the heat of ultrasonic waves followed by gas chromatography and mass spectrometry (GC–MS) analysis. Optimum levels of the variables affecting the yield of FAMEs were investigated. The results indicate that the optimal levels are 55 °C, 60 W, 10% H2SO4/CH3OH, and 50 min. Recoveries ranged from 85.32% to 112.11%, with a detection limit ranging from 0.03 to 0.08 μg mL–1. The linearity, using the linear correlation coefficient, was higher than 0.9914.


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