Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends

Cunha, Camilla L.; Torres, Anamaris; Luna, Aderval S.

doi:10.1016/j.fuel.2019.116344

Cited by 46 publications

(15 citation statements)

References 60 publications

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“…The models developed by Balabin and Smirnov using full spectrum PLS predicted the viscosity of biodiesel with a RMSEP of 0.20 mm 2 /s. The RMSEP obtained with full spectra PLS regression models developed by Cunha et al is 0.02 mm 2 /s. The RMSEP of the PLS5 model developed in the present work to predict the kinematic viscosity of biodiesel in the mid-FTIR range is 0.18 mm 2 /s, which is close to the RMSEP reported in the literature. , …”

Section: Resultsmentioning

confidence: 90%

“…Notably, many researchers ,, employed NIR spectroscopy to predict the kinematic viscosity and density of biodiesel, while the present work is based on mid-FTIR spectroscopy. However, to ascertain the applicability of the present PLS regression models, the RMSEPs of the present models were compared with those reported by other researchers. ,, Baptista et al developed full spectrum PLS models and the predicted viscosity of 48 biodiesel samples, which resulted in a RMSEP of 0.09 mm 2 /s.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Near-infrared (NIR) spectroscopy in conjunction with multivariate data analysis is used to analyze the fuel quality and predict the properties of biodiesel. , Many researchers − employed NIR spectroscopy with multivariate analysis techniques such as principal component analysis (PCA), partial least-squares (PLS) regression to predict the various properties of biodiesel including iodine value, , cold filter plugging point, ,, kinematic viscosity, ,, and density. , Similarly, the applicability of mid-FTIR spectroscopy with multivariate data analysis to predict the density, refractive index, and cold filter plugging point of biodiesel is also demonstrated in the literature . Thus, the existing literature strongly affirms the efficacy of a FTIR-spectroscopy-based approach to predict the properties of biodiesel.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fourier-Transform-Infrared-Spectroscopy-Based Approach to Predict Engine Fuel Properties of Biodiesel

Bukkarapu

Krishnasamy

2021

Energy Fuels

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Robust models are useful to predict the properties of biodiesel to facilitate the careful choice of feedstock for producing biodiesel for automotive engine applications. In the present work, a Fourier transform infrared spectroscopy (FTIR)-based approach is used to predict the kinematic viscosity, density, cetane number, and the higher calorific value of biodiesels. Unlike the standard partial least-squares (PLS) regression performed over a complete infrared spectrum, a novel approach involving few independent variables based on the functional groups present in biodiesel and correlating them with the properties of biodiesel is explored in the present study. To mimic a wide range and the type of methyl esters present in biodiesels, five biodiesels of significantly different compositions, namely, camelina, coconut, karanja, linseed, and palm, are chosen and are mixed in different volumetric proportions to obtain 70 biodiesel blends. The peak absorbance ratios of 70 biodiesel blends obtained from FTIR are correlated with the measured kinematic viscosity, density, cetane number, and the higher calorific value. The property prediction models are developed using multilinear regression and an artificial neural network whose performance is compared with that of standard full spectrum PLS regression. The results obtained show that the proposed approach is simple, reliable, and direct and provides better prediction with mean absolute percentage errors of 4.62%, 1.04%, 2.75%, and 6.85%, respectively, for the kinematic viscosity, density, higher calorific value, and cetane number of biodiesels.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fourier-Transform-Infrared-Spectroscopy-Based Approach to Predict Engine Fuel Properties of Biodiesel

Bukkarapu

Krishnasamy

2021

Energy Fuels

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“…Their models were feasible to analyze high variability of refinery samples [11]. Cunha et al (2020) used NIR data to predict physical properties of biodiesel and biodiesel blends. The properties they evaluated were cold filter plugging point (CFPP) and kinematic viscosity at 40 • C. According to them, multivariate calibration based on partial least squares (PLS), random forest (RF) and support vector machine (SVM) methods combined with variable selections tools were feasible in predicting the mentioned properties [12].…”

Section: Introductionmentioning

confidence: 99%

Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy

Wikberg

Heikkilä

Sirviö

et al. 2021

Fuels

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The European diesel fuel standard, EN590, allows a 7% (V/V) biodiesel (FAME) addition to automotive diesel fuel. The allowed addition of renewable diesel (HVO) to fossil diesel is not defined, as long as the properties of the fuel blend still meet the requirements of the standard. However, it is important to analyze the biofuels’ content in diesel fuel blends. In this article, a development procedure of a calibration method for quantification of the HVO and FAME contents in fossil diesel blends using near-infrared (NIR) spectroscopy is presented. The analytical range of quantification of biodiesel content is from 0 to 10% (V/V) and of renewable diesel content from 0 to 20% (V/V). The partial least squares (PLS) regression method for multivariable data analysis and construction of the calibration models were used to create the calibration method. The constructed PLS models obtained prediction results for all diesel fuel blends with root mean square error of prediction (RMSEP) values of 2.66% (V/V) for the HVO content quantification and 0.18% (V/V) for quantification of the FAME content. This article concludes that the calibration method is acceptable for laboratory applications in practice.

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Combining NIR spectroscopy with DD‐SIMCA for authentication and iSPA‐PLS‐DA for discrimination of ethyl route and oil feedstocks of biodiesels in biodiesel/diesel blends

da Costa,

de Fernandes,

Véras

et al. 2023

J Americ Oil Chem Soc

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The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near‐infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data‐Driven Soft Independent Modeling of Class Analogy (DD‐SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non‐target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares‐Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA‐PLS‐DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. As advantages, the proposed analytical methodology contributes to the United Nations' Sustainable Development Goal (SDG) #7 (affordable and clean energy) as well as aligns with the principles of Green Analytical Chemistry.

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Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends

Cited by 46 publications

References 60 publications

Fourier-Transform-Infrared-Spectroscopy-Based Approach to Predict Engine Fuel Properties of Biodiesel

Fourier-Transform-Infrared-Spectroscopy-Based Approach to Predict Engine Fuel Properties of Biodiesel

Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy

Combining NIR spectroscopy with DD‐SIMCA for authentication and iSPA‐PLS‐DA for discrimination of ethyl route and oil feedstocks of biodiesels in biodiesel/diesel blends

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