Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Calibration Method Procedure
2.2.1. Data Set
- Complexity of variations for multivariable component data set;
- Identification of the data set limits for minimizing the number of data;
- Data systematization.
2.2.2. Data Treatment
- Mean-centering method, used for spectrum centering. Each spectrum has been treated according to Formula (3), where x defines the spectrum variable and X stands for all variables of a spectrum data.
- Baseline correction method (Formula (4)), used for data pretreatment of the calibration data spectrum.
- Standard normal variate (SNV) method (Formula (5)), used for both spectrum centering and scaling [20].
2.2.3. PLS Model Development
2.2.4. Validation Process
- Group of RME, HVO (Bioverno) and DFO blend samples;
- Group of HVO (Bioverno) and DFO blend samples;
- Group of RME and DFO blend samples;
- Group of HVO (NEXBTL) and DFO blend samples;
- Group of AFME and DFO blend samples.
3. Results
3.1. PLS Calibration Model Results for FAME Quantification, 0–10% (V/V)
3.2. PLS Calibration Model Results for HVO Quantification, 0–20% (V/V)
4. Discussion
5. Conclusions
- Biodiesel content in all diesel fuel blends within the range 0–10% (V/V) with estimated uncertainty predictions, expressed by root mean square error of prediction (RMSEP) value, of 0.18% (V/V);
- Renewable diesel content in all diesel fuel blends within the range 0–20% (V/V) with estimated uncertainty predictions, RMSEP, of 2.66% (V/V).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- European Standard EN590:2009 (E). Automotive Fuels—Diesel—Requirements and Test Methods. Available online: https://standards.iteh.ai/catalog/standards/cen/c3d8c75c-ea89-4b8a-afc3-0fb533afbc14/en-590-2009 (accessed on 28 April 2021).
- Knothe, G. Biodiesel and renewable diesel: A comparison. Prog. Energy Combust. Sci. 2010, 36, 364–373. [Google Scholar] [CrossRef]
- Hoekman, S.K.; Broch, A.; Robbins, C.; Ceniceros, E.; Natarajan, M. Review of biodiesel composition, properties, and specifications. Renew. Sustain. Energy Rev. 2012, 16, 143–169. [Google Scholar] [CrossRef]
- EN 14214:2012+A1:2014. Liquid Petroleum Products—Fatty Acid Methyl Esters (FAME) for Use in Diesel Engines and Heating Applications—Requirements and Test Method. Finnish Petroleum Federation. 2014. Available online: https://standards.iteh.ai/catalog/standards/cen/37635022-4dce-4dc4-b6a1-d9375012cc8a/en-14214-2012a1-2014-fpra2 (accessed on 28 April 2021).
- Neste Oyj. Neste Renewable Diesel Handbook; Neste Proprietary Publication: Espoo, Finland, 2016; pp. 7–8. Available online: https://www.neste.com/sites/default/files/attachments/neste_renewable_diesel_handbook.pdf (accessed on 28 April 2021).
- Pantoja, S.S.; da Conceição, L.R.V.; da Costa, C.E.; Zamian, J.R.; Filho, G.N.D.R. Oxidative stability of biodiesels produced from vegetable oils having different degrees of unsaturation. Energy Convers. Manag. 2013, 74, 293–298. [Google Scholar] [CrossRef]
- Can, Ö.; Öztürk, E.; Yücesu, H.S. Combustion and exhaust emissions of canola biodiesel blends in a single cylinder DI diesel engine. Renew. Energy 2017, 109, 73–82. [Google Scholar] [CrossRef]
- Niemi, S.; Vauhkonen, V.; Mannonen, S.; Ovaska, T.; Nilsson, O.; Sirviö, K.; Heikkilä, S.; Kiijärvi, J. Effects of wood-based renewable diesel fuel blends on the performance and emissions of a non-road diesel engine. Fuel 2016, 186, 1–10. [Google Scholar] [CrossRef]
- Ovaska, T.; Niemi, S.; Sirviö, K.; Nilsson, O.; Portin, K.; Asplund, T. Effects of alternative marine diesel fuels on the exhaust particle size distributions of an off-road diesel engine. Appl. Therm. Eng. 2019, 150, 1168–1176. [Google Scholar] [CrossRef]
- Câmara, A.B.; de Carvalho, L.S.; de Morais, C.L.; de Lima, L.A.; de Araújo, H.O.; de Oliveira, F.M.; de Lima, K.M. MCR-ALS and PLS coupled to NIR/MIR spectroscopies for quantification and identification of adulterant in biodiesel-diesel blends. Fuel 2017, 210, 497–506. [Google Scholar] [CrossRef]
- Palou, A.; Miró, A.; Blanco, M.; Larraz, R.; Gómez, J.F.; Martínez, T.; González, J.M.; Alcalá, M. Calibration sets selection strategy for construction of robust PLS models for prediction of biodiesel/diesel blends physicochemical properties using NIR spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2017, 180, 119–126. [Google Scholar] [CrossRef] [PubMed]
- Cunha, C.L.; Torres, A.R.; Luna, A.S. Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends. Fuel 2020, 261, 116344. [Google Scholar] [CrossRef]
- Vrtiška, D.; Simacek, P. Prediction of HVO content in HVO/diesel blends using FTIR and chemometric methods. Fuel 2016, 174, 225–234. [Google Scholar] [CrossRef]
- Feora. Alfa Laval Case Story Online. 2011. Available online: http://www.feora.fi/documents/alfalaval_artikel11.pdf (accessed on 26 April 2021).
- Neste. The NExBTL Process. 2021. Available online: https://www.neste.com/about-neste/innovation/nexbtl-technology#a6a086d0 (accessed on 26 April 2021).
- Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1–17. [Google Scholar] [CrossRef]
- MATLAB Version: 8.1.0.604 R2013a. Available online: http://www.facebcok.blogspot.com/2013/04/mathworks-matlab-810604-r2013a-58-gb.html (accessed on 28 April 2021).
- Siesler, H.W. Introduction in Near-Infrared Spectroscopy: Principles, Instruments, Applications; Siesler, H.W., Ozaki, Y., Kawata, S., Heise, H.M., Eds.; WILEY-VCH Verlag GmbH: Weinheim, Germany, 2002; pp. 5–6. [Google Scholar]
- Metrohm, A.G. A Guide to Near-Infrared Spectroscopic Analysis of Industrial Manufacturing Processes; CH-9101: Herisau, Switzerland, 2013; pp. 11, 13–15. [Google Scholar]
- Gaydou, V.; Kister, J.; Dupuy, N. Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil. Chemom. Intell. Lab. Syst. 2011, 106, 190–197. [Google Scholar] [CrossRef]
- Diaz, I. Estimator and Model Selection Using Cross-Validation in Handbook of Big Data; Bühlmann, P., Drineas, P., Kane, M., van der Laan, M., Eds.; Taylor & Francis Group, LLC: Abingdon, UK, 2016; p. 225. [Google Scholar]
- Heise, H.M.; Winzen, R. Chemometrics. In Near-Infrared Spectroscopy in Near-Infrared Spectroscopy: Principles, Instruments, Applications; Siesler, H.W., Ozaki, Y., Kawata, S., Heise, H.M., Eds.; WILEY-VCH Verlag GmbH: Weinheim, Germany, 2002; p. 134. [Google Scholar]
- Boysworth, M.K.; Booksh, K.S. Aspects of Multivariate Calibration Applied to Near-Infrared Spectroscopy. In Handbook of Near-Infrared Analysis, 3rd ed.; Burns, D.A., Ciurczak, E.W., Eds.; Arizona State University: Tempe, AZ, USA, 2007; pp. 220–221. [Google Scholar]
- Alves, J.C.L.; Poppi, R.J. Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM). Talanta 2013, 104, 155–161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cunha, C.L.; Luna, A.S.; Oliveira, R.C.; Xavier, G.M.; Paredes, M.L.; Torres, A.R. Predicting the properties of biodiesel and its blends using mid-FT-IR spectroscopy and first-order multivariate calibration. Fuel 2017, 204, 185–194. [Google Scholar] [CrossRef]
- Sitoe, B.V.; Máquina, A.D.V.; De Santana, F.B.; Gontijo, L.C.; Santos, D.Q.; Neto, W.B. Monitoring of biodiesel content and adulterant presence in methyl and ethyl biodiesels of jatropha in blends with mineral diesel using MIR spectrometry and multivariate control charts. Fuel 2017, 191, 290–299. [Google Scholar] [CrossRef]
- Alves, J.C.L.; Poppi, R.J. Quantification of conventional and advanced biofuels contents in diesel fuel blends using near-infrared spectroscopy and multivariate calibration. Fuel 2016, 165, 379–388. [Google Scholar] [CrossRef]
Property | Unit | AFME | RME | Bioverno | NEXBTL * | DFO (EN 590) |
---|---|---|---|---|---|---|
Density 15 °C | kg/m3 | 880 | 883 | 813 | 780 | 820–845 |
Viscosity 40 °C | mm2/s | 4.46 | 4.49 | 3.50 | 3.00 | 2.00–4.50 |
Flash point | °C | >120 | 170 | 62 | 70 | >55 |
Water content | ppm | 537 | <30 | <30 | <30 | <200 |
CFPP | °C | 2 | −14 | −8 | −5 | +5…−20 |
Cetane number | 64 | 54 | 65 | 80 | >51 | |
OSI | h | 2 | 12 | - | - | >20 |
Model | CNUM | RMSECV | RMSEP | PRESS (v) | R2 |
---|---|---|---|---|---|
RPLS model | 7 | 0.16% | 0.36% | 0.000545 | 0.9942 |
PLS model (truncating negative values) | 7 | 0.16% | 0.36% | 0.000016 | 0.9997 |
Model | CNUM | RMSECV | RMSEP | PRESS (v) | R2 |
---|---|---|---|---|---|
RPLS model | 6 | 0.17% | 0.91% | 0.004996 | 0.9698 |
PLS model (truncating negative values) | 6 | 0.17% | 0.18% | 0.000203 | 0.9984 |
Model | CNUM | RMSECV | RMSEP | PRESS (v) | R2 |
---|---|---|---|---|---|
RPLS model | 5 | 0.80% | 0.97% | 0.004016 | 0.9950 |
PLS model (truncating negative values) | 5 | 0.80% | 0.92% | 0.00362 | 0.9943 |
Model | CNUM | RMSECV | RMSEP | PRESS (v) | R2 |
---|---|---|---|---|---|
RPLS model | 5 | 0.80% | 2.70% | 0.040502 | 0.9182 |
PLS model (truncating negative values) | 5 | 0.80% | 2.66% | 0.043316 | 0.9186 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wikberg, E.; Heikkilä, S.; Sirviö, K.; Välisuo, P.; Niemi, S.; Niemi, A. Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy. Fuels 2021, 2, 179-193. https://doi.org/10.3390/fuels2020011
Wikberg E, Heikkilä S, Sirviö K, Välisuo P, Niemi S, Niemi A. Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy. Fuels. 2021; 2(2):179-193. https://doi.org/10.3390/fuels2020011
Chicago/Turabian StyleWikberg, Elena, Sonja Heikkilä, Katriina Sirviö, Petri Välisuo, Seppo Niemi, and Antti Niemi. 2021. "Calibration Method for the Determination of the FAME and HVO Contents in Fossil Diesel Blends Using NIR Spectroscopy" Fuels 2, no. 2: 179-193. https://doi.org/10.3390/fuels2020011