Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons.

Autor: Roubehie Fissa M; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, 26000, Medea, Algeria. Electronic address: roubehiefissa.mohamed2021@gmail.com., Lahiouel Y; Laboratory of Silicates, Polymers and Nanocomposites (LSPN), Université 8 Mai 1945 Guelma, BP 401, Guelma, 24000, Algeria., Khaouane L; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, 26000, Medea, Algeria., Hanini S; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, 26000, Medea, Algeria.
Jazyk: angličtina
Zdroj: Journal of molecular graphics & modelling [J Mol Graph Model] 2023 Jun; Vol. 121, pp. 108450. Date of Electronic Publication: 2023 Mar 07.
DOI: 10.1016/j.jmgm.2023.108450
Abstrakt: The current work aimed to predict three critical properties: critical temperature (Tc), critical volume (Vc), and critical pressure (Pc) of pure hydrocarbons. A multi-layer perceptron artificial neural network (MLP-ANN) has been adopted as a nonlinear modeling technique and computational approach based on a few relevant molecular descriptors. A set of diverse data points was used to build three QSPR-ANN models, including 223 points for Tc, Vc, and 221 for Pc. The entire database was randomly split into two subsets: 80% for the training set and 20% for the testing set. A large number of 1666 molecular descriptors were calculated and then reduced by a statistical methodology based on several phases to retain them into a reasonable number of relevant descriptors, wherein about 99% of initial descriptors were excluded. Thus, the Quasi-Newton backpropagation (BFGS) algorithm was applied to train the ANN structure. The results of three QSPR-ANN models showed good precision, confirmed by the high values of determination coefficient (R 2 ) ranging from 0.9990 to 0.9945, and the low values of calculated errors, such as the Mean Absolute Percentage Error (MAPE) that ranged from 2.2497 to 0.7424% for the best three models of Tc, Vc, and Pc. The weight sensitivity analysis method was applied to know the contribution of each input descriptor individually or by class on each appropriate QSPR-ANN model. Moreover, the applicability domain (AD) method was also used with a strict limit of standardized residual values (d i  = ±2). However, the results were promising, with nearly 88% of the data points validated within the AD range. Finally, the results of the proposed QSPR-ANN models were compared with other well-known QSPR or ANN models for each property. Consequently, our three models provided satisfactory results, outperforming most of the models mentioned in this comparison. This computational approach can be applied in petroleum engineering and other related fields to accurately determine the critical properties of pure hydrocarbons: Tc, Vc, and Pc.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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