Prediction of properties of diesel/biodiesel blends by infrared spectroscopy and multivariate calibration

Lira, Liliana; Vasconcelos, Fernanda Vera Cruz de; Pereira, Claudete Fernandes; Paim, Ana Paula S.; Stragevitch, Luiz; Pimentel, Maria Fernanda

doi:10.1016/j.fuel.2009.05.028

Cited by 83 publications

(18 citation statements)

References 19 publications

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“…These bands are originated by the asymmetric axial deformation of the C-O bond. A more detailed description of the peaks observed in this region is shown in Table 1 [20,21]. With the spectra shown in Figures 3 and 4, it is possible to highlight and identify the main structural differences between diesel and biodiesel.…”

Section: Ft-ir Analyses Resultsmentioning

confidence: 99%

Quality Assessment of Biodiesel Blends Proposed by the New Mexican Policy Framework

et al. 2017

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Nowadays, biodiesel is being promoted worldwide as a sustainable and alternative to diesel fuel. However, there is still a lack of a biodiesel market in Mexico. Hence, a new initiative to reform the Mexican biofuels framework by decree includes the production and use of biodiesel. This regulation can ensure and contribute to the development of the biodiesel market in Mexico. The initiative proposes to start from the B5.8 blend by the end of 2017 and reach the B10 by 2020. Therefore, the objective of the present work was the quality assessment of biodiesel blends proposed by the new Mexican policy framework. The techniques applied were Fourier transform infrared (FT-IR) spectroscopy, X-ray fluorescence analysis, scanning electron microscopy analysis, viscosity, higher heating value, thermogravimetric analysis, refractive index, acid number, specific gravity, flash point, and copper strip corrosion based on ASTM standards. The results indicate that the biodiesel and its blends B5.8 and B10 fulfilled relevant quality specifications established in the ASTM D6751 and EN14214 standards for fuels. However, the fuel blends presented a higher heating value (HHV) diminution. The experimental HHV percentages decrease for the mandatory mixtures compared to diesel were 2.29% (B10), and 0.29% (B5.8).

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Section: Ft-ir Analyses Resultsmentioning

confidence: 99%

Quality Assessment of Biodiesel Blends Proposed by the New Mexican Policy Framework

et al. 2017

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“…Many industries use predictive models based on spectroscopic data that have been processed using chemometric methods, like partial least squares regression (PLSR) and principal component regression (PCR). Many studies have been conducted on the use of near‐infrared spectroscopy combined with PLSR to predict the viscosity of petroleum products . These studies show the potential for NIR to predict the viscosity of Newtonian liquids.…”

Section: Introductionmentioning

confidence: 99%

Use of inline near‐infrared spectroscopy to predict the viscosity of shampoo using multivariate analysis

Haroon

Arafeh

Martin

et al. 2019

Intern J of Cosmetic Sci

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ObjectiveIn the personal care industry, viscosity is a critical quality attribute that influences product quality and process economics. Like many industrial liquids, personal care liquids are complex non‐Newtonian liquids made up of aqueous surfactant systems whose viscosity depends on the build‐up of micellar networks. Measuring the viscosity of complex liquids offline is easily done using benchtop rheometers and viscometers. The challenge lies in measuring the viscosity of personal care liquids online during manufacturing. Being able to track the viscosity of such products through their manufacturing cycle will not only allow for better process control but also more enhanced quality control. Therefore, the aim of this work was to investigate how proxy measurements using inline near‐infrared (NIR) spectroscopy in transmission mode can be used to predict the viscosity of shampoo. NIR spectroscopy has not, to the best our knowledge, been used to predict the viscosity of complex surfactant systems like shampoo and could significantly affect the way quality is monitored in a manufacturing environment.MethodThis work focuses on viscosity changes because of differences in chloride content as salt is often used to adjust viscosity. The relationship between salt content and the viscosity of shampoo is well known following the salt curve. From an industrial perspective the region of interest for the formulation studied in this work only covers a small section of this curve. Therefore, two predictive models were developed: one covering the full range of the salt curve and another focusing on the industrially applicable region.ResultModels were produced using partial least squares (PLS) where both datasets showed some predictive ability with the concentrated region of interest showing enhanced performance [root mean square error of prediction (RMSEP) – 2.32 Pa s] compared with the larger range (RMSEP – 4.44 Pa s).ConclusionThis work provides a good starting point for developing robust predictive models for in situ viscosity measurements for shampoo manufacturing, where further work into different sources of variation and the extent of the modelling capability with regards to different formulations should be studied.

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“…The development of methods for accurate and fast analysis has become an urgent need to control the quality of petroleum crude oils and their fractions or cuts production processes. A large number of analytical techniques including high-performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), mass spectrometry (MS), fluorescence spectroscopy, Raman and infrared spectroscopy have been widely applied to the analysis of hydrocarbons and their derivatives [1][2][3][4][5][6][7][8]. These techniques provided excellent results in comparison with traditional methodologies of analysis; however, some of them are expensive and not always available in all laboratories.Fourier Transform Near Infrared Spectroscopy (FT-NIR) has been used as a promising alternative to replace traditional American Society for Testing and Materials methods (ASTM) to determine physicochemical properties of the petroleum crude oils and their fractions, because it requires a minimum sample treatment, the acquisition of spectra takes a few minutes and offers an acceptable repeatability for liquid and solid samples [6,9].…”

Section: Introductionmentioning

confidence: 99%

Bromine number prediction for Colombian naphthas using near-infrared spectroscopy and chemometric methods

Aparício¹,

Bermúdez²,

Cabanzo³

et al. 2016

rev.ion

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Thirty-eight naphtha samples were used to develop a chemometric method to predict bromine number. All samples were characterized by Fourier transform near infrared spectroscopy (FT-NIR), and their spectra were correlated by similarity using principal component analysis (PCA). The models for bromine number determination (BN) were established by Partial Least Squares regression (PLS) and Multiple Polynomial Regression (MPR). PCA allowed classifying the samples into the light and heavy, determining the most significant spectral variables. These variables are located in the regions between 4000-4800 and 5200-6350cm-1. The results determined by combining FT-NIR spectroscopy and chemometrics were very close to those obtained by standardized methods. This approach may be an alternative for analysis of BN, which requires sample turnarounds lower than five minutes and lower cost compared to traditional methods.

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Prediction of properties of diesel/biodiesel blends by infrared spectroscopy and multivariate calibration

Cited by 83 publications

References 19 publications

Quality Assessment of Biodiesel Blends Proposed by the New Mexican Policy Framework

Quality Assessment of Biodiesel Blends Proposed by the New Mexican Policy Framework

Use of inline near‐infrared spectroscopy to predict the viscosity of shampoo using multivariate analysis

Bromine number prediction for Colombian naphthas using near-infrared spectroscopy and chemometric methods

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