Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds. (August 2022)

• Record Type: Journal Article
• Authors: Devotta, Sukumar; Chelani, Asha
• Is Part Of: International journal of refrigeration. Volume 140(2022)
• Page Start: 112
• Page End: 124
• Publication Date: 2022-08
• Subjects: Refrigerants -- Halocarbons -- Normal boiling point -- Critical temperature -- Artificial neural network -- Group Contribution Method; Frigorigènes -- Halocarbures -- Point d'ébullition normal -- Température critique -- Réseau neuronal artificiel -- Méthode de contribution de groupe; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2022.04.020
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: Novel methods for normal boiling point and critical temperature are proposed. Data for emerging refrigerants and related compounds have been used. Artificial neural network (ANN) and group contribution method (GCM) are combined. The models are able to accurately predict sing structure of compounds. These unified and simple models are novel. Abstract: In this study, both normal boiling point and critical temperature of refrigerants and related compounds are predicted only from their molecular structures using a simple and unified Artificial Neural Network - Group Contribution Method. Identical 32 (including molecular mass) groups and methodologies have been used with 251 experimental data for TB and 132 experimental data for TC . In spite of its simplicity, the agreements between experimental and ANN predicted data for TB and TC are very good, better than most of the existing models. The percentage errors for training and test data sets are 2.4% and 3.7% and 2.8% and 5.7% for TB and TC respectively. The overall percentage errors for TB and TC are 2.8% and 3.7% respectively. A comparison of the proposed models with other models shows that for the class of compounds considered i.e., refrigerants and related compounds, this model predicts most accurately. These models can be conveniently used for any preliminary screening of compounds as alternative refrigerants or working fluids or for any other applications.