Estimating flashpoints of fuels and chemical compounds using hybrid machine-learning techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144660" target="_blank" >RIV/00216305:26210/22:PU144660 - isvavai.cz</a>

Result on the web

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0016236122011449" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0016236122011449</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fuel.2022.124292" target="_blank" >10.1016/j.fuel.2022.124292</a>

Result language

angličtina

Original language name

Estimating flashpoints of fuels and chemical compounds using hybrid machine-learning techniques

Original language description

Flashpoint of organic materials is a crucial physical property in industrial applications and laboratory experiments, which provides information on safety standards and needed precautions in handling various organic materials. Proposed methods to determine the flashpoint of an organic material suffer from dependency on other physical properties of the chemical or demand complicated calculations which are time-consuming. In this work, a direct system model is proposed to anticipate the flashpoints of organic materials for a wide range of chemical compounds. The following models of genetic algorithm-adaptive neuro-fuzzy inference system, the least-squares version of the support vector machine, particle swarm optimisation-adaptive neuro-fuzzy inference system, and artificial neural network were applied to develop the model. This system model can speed up the flashpoint determination process and its accuracy. It can also anticipate the flashpoints of new organic materials. 79 functional groups were gathered to form a group contribution method to estimate the flashpoints of various chemical compounds. The functional groups were correlated with the model, known as the committee machine intelligent system, which performs accurately to prognosticate the flashpoint for every compound. 1,378 chemical compounds of different chemical categories were used to develop the model, making it suitable to estimate the flashpoints. This system model can determine or anticipate the flashpoints of organic materials with high accuracy and provide useful information for safety considerations in laboratory and industrial applications.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

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

Name of the periodical

FUEL

ISSN

0016-2361

e-ISSN

1873-7153

Volume of the periodical

neuveden

Issue of the periodical within the volume

323

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

124292-124292

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85129568050