Prediction of HVO content in HVO/diesel blends using FTIR and chemometric methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22320%2F16%3A43901980" target="_blank" >RIV/60461373:22320/16:43901980 - isvavai.cz</a>

Result on the web

<a href="http://www.sciencedirect.com/science/article/pii/S0016236116001241" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0016236116001241</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fuel.2016.02.010" target="_blank" >10.1016/j.fuel.2016.02.010</a>

Result language

angličtina

Original language name

Prediction of HVO content in HVO/diesel blends using FTIR and chemometric methods

Original language description

Predictive models based on chemometric processing of FTIR spectra were created in order to develop a method for the determination of hydrotreated vegetable oil (HVO) in petroleum diesel/HVO blends. The blends were prepared in the entire concentration range of 0-100 wt.%. The set of samples was divided into calibration and validation sets. Four various approaches to the sample distribution were employed. There were predicted unknown content of known HVO in known diesel (approach 1), unknown content of known HVO in unknown diesel (approach 2), unknown content of unknown HVO in known diesel (approach 3) and finally unknown content of unknown HVO in unknown diesel (approach 4). The spectra obtained from FTIR measurement were processed using a software. Partial least squares regression (PLS) and principal component regression (PCR) were used to develop the predictive models. Optimal combination of preprocessing techniques was sought. In order to find suitable parameters of the preprocessing methods, hundreds of various models were tested. Although the optimal combination of preprocessing techniques was found for the most, some of them were strongly affected by preprocessing. The models with suitable preprocessing were able to predict the HVO content in given validation samples, however, the prediction of the HVO content in unknown "real" samples may be less accurate even incorrect because of the dependence on the kind of preprocessing.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JT - Propulsion, engines and fuels

OECD FORD branch

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Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Fuel

ISSN

0016-2361

e-ISSN

—

Volume of the periodical

174

Issue of the periodical within the volume

15. června 2016

Country of publishing house

US - UNITED STATES

Number of pages

10

Pages from-to

225-234

UT code for WoS article

000370530600027

EID of the result in the Scopus database

2-s2.0-84957805482