TY  - JOUR
ID  - scholars11161
TI  - Highly accurate prediction of flammability limits of chemical compounds using novel integrated hybrid models
KW  - algorithm; article; data analysis software; flammability; prediction; chemical database; chemical model; fire; quantitative structure activity relation; reproducibility
KW  -  Algorithms; Databases
KW  -  Chemical; Fires; Models
KW  -  Chemical; Quantitative Structure-Activity Relationship; Reproducibility of Results
N2  - Two novel and highly accurate hybrid models were developed for the prediction of the flammability limits (lower flammability limit (LFL) and upper flammability limit (UFL)) of pure compounds using a quantitative structureâ??property relationship approach. The two models were developed using a dataset obtained from the DIPPR Project 801 database, which comprises 1057 and 515 literature data for the LFL and UFL, respectively. Multiple linear regression (MLR), logarithmic, and polynomial models were used to develop the models according to an algorithm and code written using the MATLAB software. The results indicated that the proposed models were capable of predicting LFL and UFL values with accuracies that were among the best (i.e. most optimised) reported in the literature (LFL: R2 = 99.72, with an average absolute relative deviation (AARD) of 0.8; UFL: R2 = 99.64, with an AARD of 1.41). These hybrid models are unique in that they were developed using a modified mathematical technique combined three conventional methods. These models afford good practicability and can be used as cost-effective alternatives to experimental measurements of LFL and UFL values for a wide range of pure compounds. © 2019 El-Harbawi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
N1  - cited By 2
IS  - 11
AV  - none
VL  - 14
JF  - PLoS ONE
A1  - El-Harbawi, M.
A1  - Samir, B.B.
A1  - El blidi, L.
A1  - Ghanem, O.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075099683&doi=10.1371%2fjournal.pone.0224807&partnerID=40&md5=05b2add2d56e2bd3c15bc5bf9ff1c031
PB  - Public Library of Science
SN  - 19326203
Y1  - 2019///
ER  -