Cetane number and thermal properties of vegetable oil,biodiesel, 1-butanol and diesel blends

Vegetable oil derived fuels for diesel engines are becoming important as alternative to petroleum diesel fuels due to their environmental friendliness and availability. Ignition quality in compression ignition (CI) engines is influenced by thermal characteristics and fuel properties. In this study, the effects of vegetable oil transesterification and vegetable oil–1-butanol-diesel blends on fuel properties, cetane number (CN) and thermal characteristics were experimentally investigated. Methyl esters (biodiesel) and 10% vegetable oil–10% 1-butanol–80% diesel blends were prepared from croton oil (CRO), coconut oil (COO) and jatropha oil (JAO). CN was measured in a CFR F-5 engine, and a thermogravimetric analysis (TG), as well as the determination of fuel properties of vegetable oils, biodiesels and blends was carried out. It can be observed for vegetable oils that they possess low volatility characteristics, low CN and high viscosity different from those of biodiesels, blends and diesel fuel. It was observed that biodiesels and blends exhibit similarities with diesel in the fuel characteristics, CN and TG curves.     

Heat of combustion of biofuels mixed with fossil diesel oil

The Brazilian government has presented a biofuel program, which aims the addition of 2% of biofuel in fossil diesel in 2008 and 5% up to 2013. Thus, the knowledge of heat of combustion of biofuel/diesel blends is necessary. The biodiesel was produced by transesterification of soybean oil with a yield of 87%. The diesel-like was obtained by pyrolysis of soybean oil. This biofuel presented all parameters according to ANP. The obtained heats of combustion were 41.36 ± 0.17; 38.70 ± 0.16; and 36.71 ± 0.17 MJ/kg for diesel, diesel-like and biodiesel, respectively. The results show that the heats of combustion of biofuels are approximately 17% smaller than fossil diesel. The obtained data also show that the heats of combustion depend on the methodology used for the biofuel production. Addition of biofuels to traditional diesel fuel results in a linear decreasing of the heat of combustion with the amount of the alternative fuel added to the diesel.     

Determination of residual oil in diesel oil by spectrofluorimetric and chemometric analysis

Multivariate calibration (PLS), principal components analysis (PCA) and linear discriminant analysis (LDA), associated to synchronous spectrofluorimetry, were used to identify and quantify non-transesterified residual vegetable oil in diesel oil with the addition of 2% of biodiesel (B2). The addition of residual oil, one of the easiest ways of adultering fuel, damages engines and leads to tax evasion. Using this method, the samples of diesel oil, B2, and B2 contaminated with residual oil were classified correctly and separated into three well-defined groups. The quantification of residual oil in B2 was carried out in the 0-25% (w/w) band, RMSEC and RMSEP values ranging from 0.26 to 0.48% (w/w) and 1.6-2.6% (w/w), respectively. The method is highly sensitive and efficient to identify and quantify this type of adulterant in which 100% of the samples were correctly classified and the average relative error was approximately 4% in the range 0.5-25% (w/w).     

Heats of combustion of biofuels obtained by pyrolysis and by transesterification and of biofuel/diesel blends

The obtained heats of combustion were 46.65 ± 0.20; 38.09 ± 0.31; 39.67 ± 0.22; 41.88 ± 0.31; 37.86 ± 0.46; 39.89 ± 0.09; 41.28 ± 0.31 MJ/kg for diesel, transesterified soybean oil, pyrolysed soybean oil and crude soybean oil, transesterified palm tree oil (Elaeis sp.), pyrolysed palm tree oil, crude palm tree oil, respectively. The results show the heats of combustion of biofuels are approximately 17% smaller than traditional diesel. The data also show the heats of combustion depend on the methodology used for the biofuel production. Addition of biofuels to traditional diesel fuel results in a linear decrease of the heat of combustion with the amount of the alternative fuel added to the diesel. However, for blends with 5% biofuels, which is the limit demanded by Brazilian legislation, no significant decrease of the heat of combustion of the commercial diesel was observed.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Quality control of vegetable oil methyl esters used as diesel fuel substitutes: Quantitative determination of mono-, di-, and triglycerides by capillary GC

Vegetable oil methyl esters are increasingly being used as substitutes for petroleum-based diesel fuels. Because the presence of triglycerides and partial glycerides in the fuel as a result of incomplete transesterification of vegetable oils can lead to serious engine problems, continuous quality control of the product is essential. A rapid gas chromatographic method has been developed for the quantitative determination of mono-, di-, and triglycerides in vegetable oil methyl esters. Prior to analysis, mono-, and diglycerides were silylated with N,O-bis(trimethylsilyl)trifluoroacetamide; tridecanoin was used as internal standard. Calibration was performed by analysis of standard solutions containing mono-, di-, and triolein: the calibration plots for mono- and diglycerides showed good linearity, whereas for triglycerides no linearity was observed for triolein concentrations below 0.05 mg/ml. When a non-linear multi level calibration was employed for the quantitation of triglycerides, the method gave excellent quantitative results for mono-, di-, and triglycerides in vegetable oil methyl esters.     

Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis

This paper reports a method to simultaneously classify 7% biodiesel and 93% diesel based on vegetable oil as the source of the biodiesel. Mafurra, moringa, and cotton biodiesel blends were characterized by infrared spectroscopy with partial least square discriminant analysis. The efficiency of model was evaluated based on the sensitivity and specificity. The model showed excellent results as all samples were correctly classified based on the raw material. Hence, the sensitivity and specificity parameters showed values of 1, which means 100% correct characterization of the samples in the calibration and prediction sets. Therefore, infrared spectroscopy with partial least square discriminant analysis is suitable for the characterization of biodiesel/diesel blends.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Application of Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics for authentication of cod-liver oil

Some vegetable oils such as canola (CaO), corn (CO), soybean (SO), and walnut (WO) oils have similar color with cod liver oil (CLO), therefore, the presence of these oils was difficult to detect using naked eye. For this reason, Fourier transform infrared (FTIR) spectroscopy using horizontal attenuated total reflectance (HATR) as sampling accessory and in the combination with chemometrics was developed for detection and quantification of these vegetable oils as adulterants in CLO. The quantification of vegetable oils was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR), while the classification between pure CLO and CLOs adulterated with CaO, CO, SO, and WO was performed using discriminant analysis (DA). PLS with FTIR normal spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R²) higher than 0.99 and root mean square error of calibration (RMSEC) in the range of 0.04-0.82% (v/v). The PLS model was further used to predict the levels of these vegetable oils in independent samples for validation/prediction purpose. The root mean square error of prediction (RMSEP) values obtained were of 1.75% (v/v) (CaO), 1.39% (v/v) (CO), 1.35% (v/v) (SO), and 1.37% (v/v) (WO), respectively. The classification using DA revealed that the developed method can classify CLO and that mixed with these vegetable oils using 9 principal components.     

Characterization by TG/DTG/DSC and FTIR of frying and fish oil residues to obtain biodiesel

Biodiesel is a fuel derived from vegetable oils or wastes. It has a lot of advantages such as less offensive exhaust, more complete combustion, reducing emissions of carbon dioxide and sulfur in addition to generating employment and wealth. This biofuel can be produced through the transesterification reaction of an alcohol with a triglyceride, with the aid of a catalyst, resulting on a biodiesel as main product, glycerol, and other byproducts. The objective of this study is to determine the optimal reaction conditions for transesterification of waste frying oil and fish, varying the reaction time, the amount of catalyst and temperature, to determine which of these variables exert a greater influence on the reaction yield, and characterize biofuels obtained. For a more accurate assessment of the influence of a given variable on the reaction yield, it was performed a statistical experimental design, the full factorial of two levels with three parameters (2³) and three central points, implemented in Statistica 7.0. Regarding the transesterification of waste of the fish oil, the amount of catalyst was the variable that most influenced the reaction yield, the parameters time and temperature had negligible impact on income. Biofuels were also characterized using thermal analysis techniques and FTIR. Most reactions obtained thermogravimetric yield above 90%, a promising result.     

Thermal stability during pyrolysis of sunflower oil produced in the northeast of Brazil

This study aims to analyze thermal stability and make a rheological assessment of sunflower oil produced in the Northeast of Brazil, resulting from the pyrolysis process. Oil samples were submitted to thermal degradation and the reaction was evaluated by the thermogravimetric technique, at temperatures between 30 and 900?°C. Apparent activation energy was determined using the model-free kinetics theory. The coaxial cylinder system at operating temperature of 40?°C was used to obtain rheological parameters. Oil was characterized by gas chromatography. The lipid profile of the oil exhibited good quality. The activation energy of the sunflower oil was 201.2?kJ?mol^?1. Results showed the influence of physical?Cchemical characteristics of vegetable oil on the thermal decomposition process. Rheological analyses confirmed Newtonian rheological behavior. The high potential of the ??Catissol?? variety produced in Northeast Brazil as raw material for biofuel production using pyrolysis was also demonstrated.     

Determination of quality parameters of beers by the use of attenuated total reflectance-Fourier transform infrared spectroscopy

The estimation of important quality parameters of beers, such as original and real extracts and alcohol content, has been evaluated by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) using a partial least square (PLS) calibration approach. Two sample populations, one consisting of 24 samples and other of 21 samples, obtained from the Spanish market and covering different types of beer were used. The first set was used for building and validating the model, whereas the second, measured 6 months after, was used for evaluating its robustness. The spectral range and the size of the calibration set and its suitability for building the PLS model have been evaluated.Considering a calibration set comprised of 12 samples, selected via hierarchical cluster analysis, and a validation data set of 11 samples, the absolute mean difference (d_x−y) and standard deviation of mean differences (s_x−y) of the real extract, original extract and alcohol content were 0.009 and 0.069% (w/w), −0.021 and 0.20% (w/w) and −0.003 and 0.130% (v/v), respectively. The maximum error for the prediction of any of these three parameters for a new sample did not exceed 2.5%. These values were practically invariant for both tested data sets.The developed methodology favourably compares with the automatic reference methodology in terms of speed and reagent consumption and waste generation.     

Screening of slow pyrolysis routes for maximum biofuel production from Syzygium malaccense biomass by TGA-FSD and chemometric tools

The dependence of oil and its consequent commercial fluctuation make researchers to seek viable alternatives for their gradual replacement. The present work aims to evaluate the production of biofuels from the slow pyrolysis of the Syzygium malaccense biomass (Malay Apple) through thermogravimetric analysis (TGA-DTG), Fraser-Suzuki deconvolution and chemometric tools of multivariate analysis. Three chemical treatments (sodium hydroxide, sulfuric acid and phosphoric acid) were evaluated, as well as the variation in the heating rate and the consequent effects on the pyrolysis of biomass for the production of biofuels, totaling 16 pyrolysis routes (including the use of biomass no treatment). In the obtained data, multivariate analysis tools were used, looking for groups that presented favorable characteristics for the production of quality biofuels. Heatmap, k-means and SOM analysis indicated the routes that use phosphoric acid treatment and heating rates of 10 or 15 °C/min (Jap10 and Jap15, respectively) as relevant, because they have low lignin contents and high gaseous cellulose and hemicellulose contents.

     

Thermal investigation of biodiesel blends derived from rapeseed oil

Over the last few years, the production of biodiesel from vegetable oil has significantly increased in Romania due to its obligatory use in the composition of diesel fuel. In this study, biodiesel from rapeseed oil was produced using methanol and a base catalyst. Four samples of biodiesel/diesel blends were prepared for analysis to determine the main thermal decomposition processes and calorimetric events. The thermal profiles were compared to reference diesel. The data obtained on the Thermogravimetry/Differential thermogravimetry and DTA curves show the quality of biodiesel/diesel blends and the possibility that the fuel be used in diesel engines. It was found that biodiesel blends with higher percentage of biodiesel in their compositions were more thermally stable than diesel fuel.     

Oil Production Towards Biofuel from Autotrophic Microalgae Semicontinuous Cultivations Monitorized by Flow Cytometry

Two microalgae species (Scenedesmus obliquus and Neochloris oleoabundans) were cultivated in closed sleeve photobioreactors in order to select the best oil producer for further large-scale open raceway pond cultivations, aiming at biofuel production. Scenedesmus obliquus reached a higher maximum biomass concentration (1.41 g l⁻¹) with a lower lipid content (12.8% w/w), as compared to N. oleoabundans [maximum biomass concentration of 0.92 g l⁻¹ with 16.5% (w/w) lipid content]. Both microalgae showed adequate fatty acid composition and iodine values as substitutes for diesel fuel. Based on these results, N. oleoabundans was selected for further open raceway pond cultivations. Under these conditions, N. oleoabundans reached a maximum biomass concentration of 2.8 g l⁻¹ with 11% (w/w) of lipid content. A high correlation between the Nile Red fluorescence intensity measured by flow cytometry and total lipid content assayed by the traditional gravimetric lipid analysis was found for both microalgae, making this method a suitable and quick technique for the screening of microalgae strains for lipid production and optimization of biofuel production bioprocesses. Medium growth optimization for enhancement of microalgal oil production is now in progress.     

Multivariate near infrared spectroscopy models for predicting the iodine value, CFPP, kinematic viscosity at 40 degrees C and density at 15 degrees C of biodiesel

Biodiesel is one of the main alternatives to fossil diesel. It is a non-toxic renewable resource, which leads to lower emissions of polluting gases. In fact, European governments are targeting the incorporation of 20% of biofuels in the fossil fuels until 2020.Chemically, biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol, in the presence of a catalyst. The European Standard (EN 14214) establishes 25 parameters that have to be analysed to certify biodiesel quality and the analytical methods that should be used to determine those properties.This work reports the use of near infrared (NIR) spectroscopy to determine some important biodiesel properties: the iodine value, the cold filter plugging point, the kinematic viscosity at 40 °C and the density at 15 °C. Principal component analysis was used to perform a qualitative analysis of the spectra and partial least squares regression to develop the calibration models between analytical and spectral data. The results support that NIR spectroscopy, in combination with multivariate calibration, is a promising technique applied to biodiesel quality control, in both laboratory and industrial-scale samples.     

Analysis of free and esterified sterols in vegetable oil methyl esters by capillary GC

The introduction of quality standards for vegetable oil methyl esters is gaining in importance due to their increased use as diesel fuel substitutes and as technical products. Free and esterified sterols, the main constituents of the unsaponifiable matter in vegetable oils, are recovered in vegetable oil methyl esters and may influence the technical properties of vegetable oil methyl ester products. A rapid gas chromatographic method for the qualitative and quantitative determination of free and esterified sterols in vegetable oil methyl esters has therefore been developed. The concentration of the free sterols as well as their qualitative and quantitative composition and the concentration of the sterol esters have been determined in rape seed oil methyl ester samples by GC–FID. Prior to analysis, the free sterols were silylated with N,O-bis(trimethylsilyl)trifluoroacetamide with 1% of trimethylchlorosilane; betulinol was used as an internal standard. Calibration was performed by analysis of standard solutions containing β-sitosterol, cholesteryl stearate, and betulinol. The reproducibility of the quantitative results has been evaluated by repeated injections of the same test solution and by repeated complete analysis of the same sample.     

Multivariate near infrared spectroscopy models for predicting the methyl esters content in biodiesel

Biodiesel is the main alternative to fossil diesel. The key advantages of its use are the fact that it is a non-toxic renewable resource, which leads to lower emissions of polluting gases. European governments are targeting the incorporation of 20% of biofuels in the general fuels until 2020.Chemically, biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils/fats react with an alcohol, in the presence of a catalyst. The European Standard (EN 14214) establishes 25 parameters that have to be analysed to certify biodiesel quality and the analytical methods that should be used to determine those properties.This work reports the use of near infrared (NIR) spectroscopy to determine the esters content in biodiesel as well as the content in linolenic acid methyl esters (C18:3) in industrial and laboratory-scale biodiesel samples. Furthermore, calibration models for myristic (C14:0), palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) acid methyl esters were also obtained. Principal component analysis was used for the qualitative analysis of the spectra, while partial least squares regression was used to develop the calibration models between analytical and spectral data. The results confirm that NIR spectroscopy, in combination with multivariate calibration, is a promising technique to assess the biodiesel quality control in both laboratory-scale and industrial scale samples.     

Near-infrared diffuse reflectance spectroscopy and neural networks for measuring nutritional parameters in chocolate samples

A rapid and non-destructive method has been developed for the characterization of chocolate samples based on diffuse reflectance near-infrared Fourier transform spectroscopy (DRIFTS) and artificial neural networks (ANNs). This methodology provides a potentially useful alternative to time-consuming chemical methods of analysis. To assess its utility, 36 chocolate samples purchased from the Spanish market were analyzed for the determination of the main nutritional parameters like carbohydrates, fat, proteins, energetic value and cocoa content.Direct triplicate measurements of each sample were carried out by DRIFTS. Cluster hierarchical analysis was used for selecting calibration and validation data sets, resulting in a calibration set comprised of 19 samples and a validation data set of 17 samples. As it is common the presence of non-linear effect in reflectance spectroscopy, ANNs was chosen for data pretreatment. The root-mean-square error of prediction (RMSEP) values obtained for carbohydrates, fat, energetic value and cocoa were 1.0% (w/w), 1.0% (w/w), 50 kJ (100 g)⁻¹ and 1.4%, respectively. The mean difference (d_x-y) and standard deviation of mean differences (s_x-y) of the carbohydrates, fat, proteins content, energetic value and cocoa content were 0.9 and 2.4% (w/w), 0.2 and 1.0% (w/w), 9.1 and 50 kJ (100 g)⁻¹, and −0.5 and 1.4%, respectively. The maximum relative error for the prediction (QC) of any of these parameters for a new sample did not exceed 5.2%.