Rapid Models for Predicting the Low-Temperature Behavior of Diesel

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62243136%3A_____%2F19%3AN0000010" target="_blank" >RIV/62243136:_____/19:N0000010 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62243136:_____/19:N0000033

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ceat.201800549" target="_blank" >https://onlinelibrary.wiley.com/doi/abs/10.1002/ceat.201800549</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ceat.201800549" target="_blank" >10.1002/ceat.201800549</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rapid Models for Predicting the Low-Temperature Behavior of Diesel

Popis výsledku v původním jazyce

"The cold filtration plugging point (CFPP) is the method most commonly applied to characterize the low-temperature behavior of diesel and its components. However, this method is time-consuming and does not have good repeatability, especially for samples with very low CFPP values like kerosene, light cycle oil, etc. Three new models for CFPP prediction were developed and compared: a combined density and distillation curve, differential scanning calorimetry, and nearinfrared. A set of 133 samples of diesel components were used to create the models, containing streams from different sources and levels of treatment. A further 28 diesel samples were used to validate and compare the models. All three models not only were faster than the standard method but also were found to be in good agreement with CFPP values. Each model has its own particular advantages suiting it to a particular type of diesel sample and stage of the diesel production process."

Název v anglickém jazyce

Rapid Models for Predicting the Low-Temperature Behavior of Diesel

Popis výsledku anglicky

"The cold filtration plugging point (CFPP) is the method most commonly applied to characterize the low-temperature behavior of diesel and its components. However, this method is time-consuming and does not have good repeatability, especially for samples with very low CFPP values like kerosene, light cycle oil, etc. Three new models for CFPP prediction were developed and compared: a combined density and distillation curve, differential scanning calorimetry, and nearinfrared. A set of 133 samples of diesel components were used to create the models, containing streams from different sources and levels of treatment. A further 28 diesel samples were used to validate and compare the models. All three models not only were faster than the standard method but also were found to be in good agreement with CFPP values. Each model has its own particular advantages suiting it to a particular type of diesel sample and stage of the diesel production process."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

20402 - Chemical process engineering

Projekt

<a href="/cs/project/LO1606" target="_blank" >LO1606: Rozvoj Centra UniCRE</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Chemical Engineering & Technology

ISSN

1521-4125

e-ISSN

—

Svazek periodika

42

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

735-743

Kód UT WoS článku

000462235100004

EID výsledku v databázi Scopus

2-s2.0-85061305939