Abstract
This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.
Highlights
Physical properties of chemical compounds, such as critical properties and acentric factor are important for scientists in designing and modelling chemical processes[1] such as supercritical extraction and their importance in the application of different equation of states
The results suggested that hybrid support vector machine (SVM) with Dragonfly optimisation algorithm (SVM-Dragonfly optimisation algorithm (DA)) had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively
W1S, j are input-hidden layer and hidden-output layer weights matrix, and bhj, bO1 are biases of hidden and output layers, respectively. Another model was developed based on the SVM approach, the best model was based on the optimisation of its parameters, namely, capacity (Box Constraint) (C), the kernel width parameter (Kernel scale) (γ), the quantity of support vectors (QSV) and the kernel function
Summary
Physical properties of chemical compounds, such as critical properties and acentric factor are important for scientists in designing and modelling chemical processes[1] such as supercritical extraction and their importance in the application of different equation of states. Since group contribution based models have some limitations,[2] another approach based on QSPR was used to estimate the physical properties solely from the molecular descriptors, which are computed by applying certain mathematical algorithms on the molecular structure of components.[2,3,4] Several approaches have been employed to estimate these properties, namely, group contribution from molecular structures,[5,6] multi-linear regression based on QSPR approaches from descriptors.[1] In addition to different computational techniques like artificial neural networks (ANNs),[7,8] adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) have been successfully used to estimate these properties of different components from descriptors.[1,9,10,11,12]
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