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

Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype’s PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device’s potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control.

Bifunctional Co3O4/ZSM-5 Mesoporous Catalysts for Biodiesel Production via Esterification of Unsaturated Omega-9 Oleic Acid

In the present work, two sets of the Co/ZSM-5 mesoporous catalysts with different acidity and Co loadings varying from 1 to 5 and 10 wt% were prepared using mesoporous ZSM-5-A (Si/Al = 50) and ZSM-5-B (Si/Al = 150) as support. X-ray diffraction (XRD) analysis showed that the Co3O4 phase was formed in the surface of catalysts and the reducibility of Co3O4 nanoparticles on the ZSM-5-B was greater in comparison with that on the ZSM-5-A solid. In situ FTIR of pyridine adsorption characterization confirmed that all of the Co/ZSM-5 catalysts contained both Lewis (L) and Brønsted (B) acid sites, with a relatively balanced B/L ratio ranging from 0.61 to 1.94. Therefore, the Si/Al molar ratio in ZSM-5 affected both the surface acidity and the cobalt oxide reducibility. In the esterification of unsaturated omega-9 oleic acid with methanol, under the optimal reaction conditions (temperature 160 °C, catalyst concentration 2 g/L, methanol/oleic acid molar ratio 30, and reaction time 180 min), the biodiesel selectivity reached 95.1% over the most active 10 wt% Co/ZSM-5-B catalyst. The higher esterification activity of the Co/ZSM-5-B catalysts can be correlated with the greater amount of B and L acid sites, the balanced B/L ratio, and the higher reducibility of Co3O4 nanoparticles. The oleic acid esterification reaction followed the bifunctional mechanism of combining metal function (dispersed Co3O4 with a greater reducibility) with the acidity function (both B and L acid sites with a relative balanced B/L ratio) on the catalysts, which may help in providing a deep understanding of the esterification pathways and benefiting the design of novel bifunctional catalysts for biofuel production.

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

An optical diagnostic, based on laser-induced fluorescence (LIF), has been developed for on-engine measurements of real-time fuel dilution of engine oil or fuel in oil (FIO). Fuel dilution of oil is broadly relevant to advancing engine technology including durability, calibration, and catalyst-system management, and believed to promote destructive stochastic pre-ignition (SPI) during high-load engine operations. While standard (e.g., ASTM D3524-90) methods are not capable of real-time transient measurements, the LIF technique resolves transient dilution on the minutes time scale. We have expanded on our original FIO instrument development by introducing an improved analysis based on multivariate least square chemometrics analysis. The measurement uses a fuel dye (180-1300 parts per million, by mass) and monitors for its presence in the oil using 532 nm excitation and LIF. While the original FIO instrument utilized a two-color ratio method for analysis, the improved chemometric analysis uses the fully resolved LIF dye spectra to provide better predictive FIO accuracy (>92%) over a wide FIO range (1.5-14%) typical of engine application. We also investigate the effect of oil temperature on the LIF signal. Limited engine applications for demonstrating and validating the improved FIO instrument are shown, and the related data used to quantify practical detection limit and sensitivity. The improved analysis is insensitive to laser power fluctuation and change in detector integration time, providing an excellent FIO sensitivity (1-2%) and detection limit (0.01 %FIO) over a wide range of loads and injection timings, illustrating this updated approach to be a promising tool for advancing engine technology.

Analysis of 1H NMR spectra of diesel and crambe biodiesel mixtures using chemometrics tools to evaluate the authenticity of a Brazilian standard biodiesel blend

A methodology was developed to monitor the content of crambe biodiesel in mixtures with conventional diesel using hydrogen nuclear magnetic resonance (¹H NMR) spectroscopy combined with the orthogonal projections on the latent structure-discrimination analysis (OPLS-DA). The efficiency of the developed OPLS-DA model was analyzed based on the criteria of true response statistics: false positive and false negative rate, sensitivity, specificity, efficiency and Matthew's correlation coefficient, where the sensitivity (true positive rate) and specificity (true negative rate) were both equal to 1 and the false positive and false negative rates were both equal to 0, which means that all samples to be predicted as belonging to the diesel class of interest, B10 (containing 10% biodiesel and 90% pure diesel), were predicted in class 1, and all samples to be considered as belonging to the diesel class, not of interest, BX (biodiesel content less and greater than in B10), were predicted in class 0. These results showed 100% correct classification of the training and test set samples for B10 and BX, demonstrating a high efficiency of the OPLS-DA model in the monitoring of crambe methyl biodiesel content when mixed with diesel in various proportions. The excellent results in the application of this model suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.

Nondestructive Near-Infrared Spectroscopic Analysis of Oils on Wood Surfaces

The further use of wood resources is expected in an environmentally conscious society. Added-value, such as durability enhancement and preservation by painting, are needed to expand the applicability of wood. Assessment of wood properties such as surface and coat adhesion can be made by studying perviousness to liquid oils, with the aim of developing wood products that deter insects and are weather-resistant; hence, discriminant analysis of oil type is important. Near-infrared (NIR) spectroscopy is a powerful tool for nondestructive characterization of organic materials and has been widely used in many industries. Here, NIR detection of oil on wood surfaces is applied for the distinguishing of three different types of oil (hereafter, “Oil_1”, “Oil_2” and “Oil_3”) via soft independent modeling of class analogy (SIMCA). Oil_1 was antiseptic vehicle or cutting oil. Oil_2 was used as a motor oil for an oil pressure machine. Oil_3 was plant-derived oil. Two types of wood that are commonly used in Japanese construction (Cryptomeria japonica and Chamaecyparis obtuse) were analyzed after applying oil. The NIR spectra measured after the oil was applied were greater in the ranges 1700–1800 nm and 2300–2500 nm than spectra for the bare wood sample. As SIMCA analyses were performed by using spectral data that included the moving average, baseline correction and second derivatives, good results were obtained for Oil_3 for both wood samples. However, the correct classification percentages were low for Oil_1, and the percentage of samples classified within several categories was high. If the components are very different, such as those for Oil_3, NIRS can be a powerful non-destructive method for identifying oil in the context of wood products testing.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm^-1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

A flow-based procedure exploiting the lab-in-syringe approach for the determination of ester content in biodiesel and diesel/biodiesel blends

The ester content is an important parameter to be monitored in biodiesel for evaluation of the transesterification reaction yield and for assessing the purity of the final product. This is also a relevant quality parameter in diesel/biodiesel blends to avoid frauds, because legislation establishes a minimum amount of biodiesel to be added to diesel. The official method EN14103 requires the addition of an alternative internal standard (methyl nonadecanoate) for analysis of biodiesel from bovine tallow because the methyl heptadecanoate is found in high amounts in this product. In this work, it is proposed a fast, simple, practical, and environmental friendly flow-based spectrophotometric procedure, which exploits the formation of the violet complex between Fe(III) and the hydroxamate generated by the reactions of the alkyl esters with hydroxylamine. All involved steps are carried out inside the syringe pump of a sequential injection analyzer (lab-in-syringe approach). A single phase is attained by using ethanol as mediator solvent between the organic sample and aqueous soluble reagents. Linear responses for biodiesel samples and diesel/biodiesel blends were obtained from 4-99%(v/v) to 2.0-40%(v/v) methyl esters, described by the equations: A = 0.342 + 0.00305C (r = 0.997) and A = 0.174 + 0.00503C (r = 0.999), respectively. The analytical curve can be obtained by in-line dilution of a methyl linoleate stock solution. For biodiesel samples, the coefficient of variation (n = 10), limit of detection (99.7% confidence level), and sampling rate were estimated at 0.8%, 0.36%(v/v), and 15h^-1, respectively, whereas the corresponding values for the blend samples were 0.20%, 0.03%(v/v), and 12h^-1, respectively. The procedure consumes only 860μg of hydroxylamine, 366μg of Fe₂(SO₄)₃·H₂O, and 2.0mL ethanol and generates ca. 3.0mL of residue per determination. The results agreed with those obtained by official methods EN14103/2011 e EN14078, at the 95% confidence level.

In-line near-infrared (NIR) and Raman spectroscopy coupled with principal component analysis (PCA) for in situ evaluation of the transesterification reaction

Biodiesel was synthesized from different commercially available oils while in-line Raman and near-infrared (NIR) spectra were obtained simultaneously, and the spectral changes that occurred during the reaction were evaluated with principal component analysis (PCA). Raman and NIR spectra were acquired every 30 s with fiber optic probes inserted into the reaction vessel. The reaction was performed at 60-70 °C using magnetic stirring. The time of reaction was 90 min, and during this time, 180 Raman and NIR spectra were collected. NIR spectra were collected using a transflectance probe and an optical path length of 1 mm at 8 cm(-1) spectral resolution and averaging 32 scans; for Raman spectra a 3 s exposure time and three accumulations were adequate for the analysis. Raman spectroscopy showed the ester conversion as evidenced by the displacement of the C=O band from 1747 to 1744 cm(-1) and the decrease in the intensity of the 1000-1050 cm(-1) band and the 1405 cm(-1) band as methanol was consumed in the reaction. NIR spectra also showed the decrease in methanol concentration with the band in the 4750-5000 cm(-1) region; this signal is present in the spectra of the transesterification reaction but not in the neat oils. The variations in the intensity of the methanol band were a main factor in the in-line monitoring of the transesterification reaction using Raman and NIR spectroscopy. The score plot of the first principal component showed the progress of the reaction. The final product was analyzed using (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and using mid-infrared spectroscopy, confirming the conversion of the oils to biodiesel.

Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

Predicting feedstock and percent composition for blends of biodiesel with conventional diesel using chemometrics and gas chromatography-mass spectrometry

The two main goals of the analytical method described herein were to (1) use principal component analysis (PCA), hierarchical clustering (HCA) and K-nearest neighbors (KNN) to determine the feedstock source of blends of biodiesel and conventional diesel (feedstocks were two sources of soy, two strains of jatropha, and a local feedstock) and (2) use a partial least squares (PLS) model built specifically for each feedstock to determine the percent composition of the blend. The chemometric models were built using training sets composed of total ion current chromatograms from gas chromatography-quadrupole mass spectrometry (GC-qMS) using a polar column. The models were used to semi-automatically determine feedstock and blend percent composition of independent test set samples. The PLS predictions for jatropha blends had RMSEC=0.6, RMSECV=1.2, and RMSEP=1.4. The PLS predictions for soy blends had RMSEC=0.5, RMSECV=0.8, and RMSEP=1.2. The average relative error in predicted test set sample compositions was 5% for jatropha blends and 4% for soy blends.

Thermal lens spectroscopy for the differentiation of biodiesel-diesel blends

Thermal lens (TL) spectroscopy was applied to biofuels to test its potential to distinguish diesel from biodiesel in blended fuels. Both the heat and mass diffusion effects observed using a TL procedure provide significant information about biodiesel concentrations in blended fuels. The results indicate that the mass diffusivity decreases 32% between diesel and the blend with 10% biodiesel added to the diesel. This simple TL procedure has the potential to be used for in loco analyses to certify the mixture and quality of biodiesel-diesel blends.

Quantification of ethanol in ethanol-petrol and biodiesel in biodiesel-diesel blends using fluorescence spectroscopy and multivariate methods

Ethanol blended petrol and biodiesel blended diesel are being introduced in many countries to meet the increasing demand of hydrocarbon fuels. However, technological limitations of current vehicle engine do not allow ethanol and biodiesel percentages in the blended fuel to be increased beyond a certain level. As a result quantification of ethanol in blended petrol and biodiesel in blended diesel becomes an important issue. In this work, calibration models for the quantification of ethanol in the ethanol-petrol and biodiesel in the biodiesel-diesel blends of a particular batch were made using the combination of synchronous fluorescence spectroscopy (SFS) with principal component regression (PCR) and partial least square (PLS) and excitation emission matrix fluorescence (EEMF) with N-way Partial least square (N-PLS) and unfolded-PLS. The PCR, PLS, N-PLS and unfolded-PLS calibration models were evaluated through measures like root mean square error of cross-validation (RMSECV), root mean square error of calibration (RMSEC) and square of the correlation coefficient (R(2)). The prediction abilities of the models were tested using a testing set of ethanol-petrol and biodiesel-diesel blends of known ethanol and biodiesel concentrations, error in the predictions made by the models were found to be less than 2%. The obtained calibration models are highly robust and capable of estimating low as well as high concentrations of ethanol and biodiesel.