Prediction of global warming potentials of refrigerants and related compounds from their molecular structure – An artificial neural network with group contribution method

Global Warming Potentials (GWPs) of refrigerants and related compounds are calculated by complex simulation and periodically updated by World Meteorological Organisation (WMO). A new attempt has been made to predict GWPs of such compounds based on their molecular structure using Artificial Neural Network (ANN) and Group Contribution Method (GCM). The main focus is on refrigerants, including CFCs, HCFCs, HFCs, HFOs, HCs, chlorocarbons, fluorocarbons, bromocarbons, iodocarbons, oxygenated and unsaturated halocarbons. In order to give some statistical advantage to the ANN model, many other related families of compounds, totally about 495 compounds, have also been considered. Only GWPs for the time horizon of 100 years are used in this analysis. Considering the complexities of the problem and the confidence limits on GWP data published by WMO (2018), the proposed ANN with GCM model is able to predict the GWP within reasonable accuracy. A comparative analysis with another complex prediction method establishes the superiority of this direct GWP predictions from the molecular structure of a compound.