Molecular descriptors-based models for estimating net heat of combustion of chemical compounds

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@Article{DASHTI:2021:Energy,

• author = "Amir Dashti and Omid Mazaheri and Farid Amirkhani and Amir H. Mohammadi",
• title = "Molecular descriptors-based models for estimating net heat of combustion of chemical compounds",
• journal = "Energy",
• volume = "217",
• pages = "119292",
• year = "2021",
• ISSN = "0360-5442",
• DOI = "doi:10.1016/j.energy.2020.119292",
• URL = "https://www.sciencedirect.com/science/article/pii/S0360544220323999",
• keywords = "genetic algorithms, genetic programming, Heat of combustion, QSPR, Molecular descriptor, Model, Prediction",
• abstract = "The heating values of fuels are determined by Heat of Combustion (?HCa)in which the higher amount is more lucrative. Moreover, one of the best methods to compare the stabilities of chemical materials is using ?HCa. Therefore, improving precise and general models to estimate this property in different areas such as industries and academic perspective should be considered. In this study, three models namely Least Square Support Vector Machine optimized by Coupled Simulated Annealing optimization algorithm (CSA-LSSVM), Genetic Programming (GP) and Adaptive-Neuro Fuzzy Inference System optimized by PSO, and GA methods (PSO-ANFIS and GA-ANFIS) were applied to estimate ?HCa Also, ?HCa can be expressed by the GP model with an equation. The input parameters of the models are total carbon atoms in a molecule (nC), sum of atomic van der Waals volumes (scaled on carbon atom) (Sv), Broto-Moreau autocorrelation of a topological structure (ATS2m), and total Eigenvalue from electronegativity weighted distance matrix (siege). In addition, two parameter models based on measureable variables of nC and Sv are proposed. In a comprehensive set, 1714 data points were used to fulfill and develop the models. Results demonstrate that the models are trustworthy and accurate (especially the PSO-ANFIS model) in comparison with other recently developed literature models",

}

Genetic Programming entries for Amir Dashti Omid Mazaheri Farid Amirkhani Amir H Mohammadi

Citations