Highly accurate prediction of flammability limits of chemical compounds using novel integrated hybrid models

Autoři: Mohanad El-Harbawi aff001;  Brahim Belhaouari Samir aff002;  Lahssen El blidi aff001;  Ouahid Ben Ghanem aff003
Působiště autorů: Department of Chemical Engineering, King Saud University, Riyadh, Saudi Arabia aff001;  Division of Information & Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar aff002;  Department of process plant operations, Qatar Technical, Doha, Qatar aff003;  Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh, Perak, Malaysia aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224807


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 structure–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.

Klíčová slova:

Artificial neural networks – Gases – Genetic algorithms – Molecular structure – Organic compounds – Polynomials – Support vector machines


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Článek vyšel v časopise


2019 Číslo 11