Quantification of Ethanol in Biodiesels Using Mid-Infrared

Article pubs.acs.org/IECR

Quantification of Ethanol in Biodiesels Using Mid-Infrared Spectroscopy and Multivariate Calibration Eloiza Guimaraẽ s,† Lucas C. Gontijo,*,†,‡ Hery Mitsutake,† Felipe B. Santana,† Douglas Q. Santos,§ and Waldomiro Borges Neto† †

Institute of Chemistry, Federal University of Uberlândia, 38408-100 Uberlândia, Brazil Goiano Federal Institute of Education, Science and Technology, 75790-000 Urutaí, Brazil § Technical School of Health, Federal University of Uberlândia, 38400-902 Uberlândia, Brazil ‡

ABSTRACT: In this work, we developed a method to quantify ethanol in ethyl soybean biodiesel and ethyl biodiesel from waste frying oil using mid-infrared spectroscopy in association with multivariate calibration by the partial least-squares (PLS) method. The obtained models are efficient in ethanol determination in concentrations ranging from 0.14% to 1.00% (w/w). In both PLS models low values of the root-mean-square error prediction (0.02%, w/w) and excellent correlation between measured and predicted values in the prediction set (R > 0.99) were observed, and there were no systematic errors according to the ASTM E1655 standard. The methods were validated according to international and national guidelines by the estimate of figures of merit, such as accuracy, linearity, sensitivity, selectivity, analytical sensitivity, and limits of detection and quantification. Considering these good results, the proposed method can be used for biofuel quality control in a fast, simple, and nondestructive way.

1. INTRODUCTION Biodiesel is a biodegradable fuel derived from renewable sources such as vegetable oils and animal fats.1,2 The oilseed species most frequently used for biodiesel production are soybean, cotton, palm, and rapeseed. Apart from vegetable oils, animal fats and waste frying oil can be used too.3 The raw material used in biodiesel production has the highest cost, so oils from nonfood sources and residual ones can significantly reduce the price of the final product.4 Biodiesel production occurs mainly through transesterification reactions, in acidic and alkaline media, where a triglyceride and an alcohol with the presence of a catalyst will form esters and glycerol.5 The most used alcohol in biodiesel production, until now, has been methanol, because it is more reactive. However, it is toxic, has a low flash point, and is made from a nonrenewable source of energy, so the ethanol route is safer (less toxic), and ethanol is made of a renewable source, producing a biodiesel with higher cetane index and lubricity.6 In Brazil, the use of ethanol in biodiesel production has more advantages, because the country is considered a reference in technology to production and transport, with a consolidated market.7 However, transesterification reactions do not directly produce alkyl fatty acid esters in the minimum specifications required to be used as fuel. Therefore, it is necessary to apply some purification steps after the transesterification. The alcohol used in excess is entrained by glycerol; however, it remains mixed with the less dense phase containing the esters.8 Thus, quality control of the final product is a very important task, because of the presence of impurities that can significantly affect engine performance and therefore reduce the flash point, cetane index, and lubricity of biodiesel, causing corrosion problems.9 © 2014 American Chemical Society

The National Petroleum Agency for Natural Gas and Biofuels of Brazil (ANP) establishes the maximum allowable amount of residual ethanol or methanol in biodiesel as 0.20% (w/w), whose standard method of determination is gas chromatography (GC), which requires the use of reagents and sample preparation.3 The quantitative determination of biofuels using simple, fast, and low-cost methods based on infrared spectroscopy combined with chemometric methods has been reported. Gontijo et al.10 have quantified soybean biodiesels in diesel blends using mid-infrared spectroscopy, and multivariate calibration demonstrated that the selection of specific spectral regions is unnecessary for the determination of biodiesels in diesel blends, which leads to a simplification and time reduction in both analysis and processing. Mendes et al.11 employed Fourier transform (FT) near-infrared and FT-Raman spectroscopies for determination of ethanol in fuel ethanol and beverages, demonstrating that these technique presented better results than GC in evaluating the ethanol content. Some studies to determine alcohol in biodiesel can be found in the scientific literature. For example, Kumar and Mishra12 quantified ethanol in biodiesel and biodiesel−diesel blends using fluorescence spectroscopy and multivariate methods and obtained root-mean-square error of prediction (RMSEP) and root-mean-square error of calibration (RMSEC) values lower than 2%, which indicates that all the calibration models have fitted the calibration data accurately and therefore predicted the concentrations of unknown samples with a small error of prediction. Felizardo et al.13 analyzed the effect of a Received: Revised: Accepted: Published: 13575

May 20, 2014 July 28, 2014 August 9, 2014 August 10, 2014 dx.doi.org/10.1021/ie502067h | Ind. Eng. Chem. Res. 2014, 53, 13575−13580

Industrial & Engineering Chemistry Research

Article

preprocessing technique used prior to the application of partial least-squares and principal component regressions in the quality of the calibration models, developed to relate the near-infrared spectra of a sample of biodiesel and its content of methanol. The best partial least-squares (PLS) model for the prediction of methanol showed an RMSEP of 61 mg·kg−1. Dorado et al.14 employed visible and NIR spectroscopies for determining methanol in biodiesel. The best PLS model presented a root-mean-square error of cross-validation value (RMSECV) equal to 0.013% (w/w) when employed in the visible and NIR ranges. Thus, it becomes necessary to develop methodologies mainly for direct determination of ethanol in biodiesels. Therefore, in this work we aimed to use mid-infrared spectroscopy combined with multivariate calibration by PLS to quantify residual ethanol in ethyl biodiesel samples from refined soybean oil and waste frying oil. In this sense, the proposed methodologies represent a viable, efficient, rapid, and nondestructive alternative able to follow the specifications established by the ANP.

of the model of ethyl biodiesel from waste frying oil were prepared. 2.3. HATR-MIR Analysis. The spectra were obtained in triplicate in the region between 4000 and 600 cm−1 in a PerkinElmer Spectrum Two spectrometer using a horizontal attenuated total reflectance (HATR) accessory for samples with a ZnSe crystal, 4 cm−1 resolution, and 16 scans. Between sample readings, the accessory was cleaned with isopropyl alcohol and residue removal was monitored by the equipment software. The spectral baselines were corrected using the baseline method. 2.4. Chemometric Analysis of MIR Spectra. Two PLS models were constructed to quantify ethanol in ethyl soybean biodiesel (model a) and ethyl biodiesel from waste frying oil (model b). In both models, the data were mean centered and the leave-one-out cross-validation method was employed for external validation. Model a was built with 37 samples in the calibration set and 25 samples in the prediction set, while for model b, 39 samples were employed in the calibration set and 23 samples in the prediction set. The number of latent variables was chosen according to the percentage of variance explained in the X (absorbance) and Y (concentration) blocks on joint comparison with RMSECV. The existence of outliers was verified by identification of samples with high leverage values and residuals in the spectral data. The leverage is defined as a measure of the influence (hi) of each sample in the model. Thus, samples with hi greater than the limit value (h) must be removed from the model. The value of h was calculated using eq 1, where nc is the number of calibration samples and LVs the number of latent variables.16

2. EXPERIMENTAL SECTION 2.1. Biodiesel Production and Characterization. The soybean refined oil used in the synthesis of biodiesel was acquired in the local market, and waste frying oils were collected from local restaurants. Biodiesels were produced at the Biofuel Laboratory of the Institute of Chemistry, Federal University of Uberlândia (LABIO-UFU). To obtain the biodiesels, 500.0 g of oil and a mixture containing potassium ethoxide (150.0 g of ethanol (PA) and 5.0 g of KOH) were stirred at 400 rpm. After 1 h, the resultant solution was concentrated in a rotary evaporator to remove excess alcohol. Then the solution was allowed to stand for 24 h to separate production residues and coproducts. After the two-layer separation, the obtained esters were purified by washing with distilled water at 90 °C and drying using vacuum distillation. In a rotary evaporator at a bath temperature of 90 °C the flash point was measured hourly until a constant value was reached. For the production of biodiesel from waste frying oil, esterification was performed before the transesterification process for the purpose of reducing the level of acidity and increasing the efficiency in the conversion of ethyl esters. The methods used to characterize the biodiesel were as follows: The moisture content was analyzed using a Karl Fischer colorimetric titrator (model 831 KF) according to standard ASTM D-6304. Acidity was determined according to the official procedures recommended by the American Oil Chemists Society. Free and total glycerin fractions were determined according to the methodology described by Pisarello et al.15 The density was determined using a DA500-Kyoto densimeter according to standard ASTM D-4052, which corresponds to the Brazilian standard ABNT NBR 14065. The flash point measurements were taken in triplicate according to method ASTM D93. A Pensky-Martens closed cup device was used, with rates of 50 mL for each test. The kinematic viscosity at 40 °C was obtained according to standards ASTM D-445 and ASTM 446. The oxidative stability was analyzed on a Rancimat (model 743) from Metrohm by method EN 14112. 2.2. Sample Preparation. The samples were prepared by adding a mass of ethanol into pure biodiesels (B100) at concentrations in the range of 0.14−1.00% (w/w). A total of 62 samples of the model of ethyl soybean biodiesel and 59 samples

h = 3((LVs + 1)/nc)

(1)

Samples with high residuals were also evaluated by comparing the RMSEC with absolute errors of the individual samples. If the difference between the actual value (y) and predicted value (ŷ) is higher than 3 times the RMSEC value, the sample is considered an outlier.16 The software used to build the models was Matlab 6.1 (MathWorks Inc.) and PLS Toolbox 3.5 (Eigenvector Research). 2.5. Net Analyte Signal (NAS). In a multivariate calibration model, the concept of NAS plays an important role in determining the figure of merit, being a part of the instrumental signal that is orthogonal to the contributions of other possible constituents present in the sample.17 The NAS (ra*) was calculated for analyte a according eq 2. PNAS,a is the orthogonal projection to a given vector space for NAS, where a identifies the analyte of interest. r represents the spectrum of a given sample. R−a is a matrix of the spectral signals generated by all other analytes except a. I represents the number of calibration samples. (R−a)+ is the pseudoinverse of R−a usually computed by singular value decomposition using A factors.18 r*a = PNAS, ar = [I − R −a(R −a)+ ]r

(2)

The concentration of a in unknown samples was obtained from the spectrum r as shown in eq 3,19,20 where sa is a spectrum containing analyte a at unit concentration and sa* is its corresponding NAS. Yun, a = 13576

saTPNAS, ar saTPNAS, asa

=

saTPNAS, aPNAS, ar saTPNAS, aPNAS, asa

=

(s*a )T r*a 2 || s*a ||

(3)

dx.doi.org/10.1021/ie502067h | Ind. Eng. Chem. Res. 2014, 53, 13575−13580

Industrial & Engineering Chemistry Research

Article

Table 1. Physicochemical Properties of the Fatty Acid Ethyl Esters from Soybean Oil (FAEE) and Waste Frying Oil (FAEEwfo) property

unit

FAEE

FAEEwfo

ANP

method

moisture content acidity free glycerin total glycerin density flash point kinematic viscosity oxidative stability

mg·kg−1 mg of KOH·g−1 %, w/w %, w/w kg·m−3 °C mm2·s−1 h

212.87 0.20 0.012 0.21 892.4 172.00 4.4 3.39

195.11 0.37 0.009 0.23 877.10 176.00 4.67 5.38