Determination of Critical Properties and Acentric Factors of Petroleum Fractions Using Artificial Neural Networks

Abstract

Various correlations are available that can determine the critical properties and acentric factors of petroleum fractions. The available methods may have low accuracy in determining these properties for heavy petroleum fractions and may require further verification because, during the development of the original predictive methods, the data describing the critical properties and acentric factors of heavy hydrocarbons and petroleum fractions were not available. In this work, after a quick review of the most common correlations reported in the literature, an alternative method based on the artificial neural network (ANN) technique is proposed to predict the critical temperatures, critical pressures, critical volumes, and acentric factors of petroleum fractions, especially heavy fractions, from their specific gravity and the average normal boiling-point temperature values. Among the different neural networks reported in the literature, the feed-forward neural network method with a modified Levenberg-Marquardt optimization algorithm is used. The model is trained and tested using the data recommended in the literature for critical properties and acentric factors of C 1 -C 45 petroleum fractions. Independent data (not used in training and developing the model) are used to validate and examine the reliability of this tool. The predictions of this model are found in acceptable agreement with the data recommended in the literature, demonstrating the reliability of the ANN technique used in this work.

Formulating Nonionic Detergents via the Integrated Free Energy Model

“…Furthermore, an implementation issue with IFEM is that some of the input data, like the Δ H vap of the oil (to calculate the cohesive energy), are correlations with some uncertainty. For example, the acentric factor (ω) of C 14 , used to calculate Δ H vap , has been reported to be in the range of 0.536 to 0.643 (Mohammadi et al, ). Presented with this range, this acentric factor was tuned to a value of 0.572 to produce the predictions in this work.…”

Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network−Group Contribution Method

“…ANNs have been used extensively in various scientific and engineering problems, − including calculations/estimations of physical and chemical properties of different pure compounds. ,− These capable mathematical tools are generally applied to the study of complicated systems. − Theoretical explanations of neural networks can be found elsewhere . Using the artificial neural network toolbox of the MATLAB software (The Mathworks Inc.), a three-layer feed-forward artificial neural network (FFANN) was developed for the problem.…”

“…40,[54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] These capable mathematical tools are generally applied to the study of complicated systems. [41][42][43][44][45][46][47][48][49][50][51][52][53] Theoretical explanations of neural networks can be found elsewhere. 70 Using the artificial neural network toolbox of the MATLAB software (The Mathworks Inc.), a three-layer feed-forward artificial neural network (FFANN) was developed for the problem.…”

Comparison between Multi-Linear- and Radial-Basis-Function-Neural-Network-Based QSPR Models for The Prediction of The Critical Temperature, Critical Pressure and Acentric Factor of Organic Compounds

“…Many examples of ANN models involve the estimation of the acentric factor for petroleum fractions. These models require other physical properties, such as the refractive index, the normal boiling point and the specific gravity or the molecular weight as input parameters [ 25 , 26 , 27 ]. Other examples are the above cited studies by Gharagheizi and coworkers, who also developed their sulfuric-compound [ 19 ] and GC-ANN [ 20 ] models for the acentric factor.…”