Intelligent prediction model of ammonia solubility in designable green solvents based on microstructure group contribution

Abstract

The rapid selection of environmentally friendly and efficient solvents is critical for improving the safety, environmental protection, and efficiency of a process. In this study, a deep neural network structure was proposed to predict the solubility of ammonia in ionic liquids based on molecular structure, combined with support vector machine (SVM), random forest (RF) and deep neural network (DNN) algorithm. In this study, a group-based quantisation method for ionic liquids was proposed. On this basis, a feature preprocessing method integrating feature selection and data standardisation was proposed. Then, the eigenvectors extracted from the molecular structure were used to predict the solubility of ammonia in ionic liquids using SVM, RF and DNN models. Based on the cross-validation optimisation model structure, three models were evaluated. Results showed that the three models yielded high prediction accuracy, and that the prediction accuracy of the MLP model was higher than those of the SVM and RF models. For the MLP model, the coefficient of determination was 0.992. The model has good prediction performance and generalisation ability. Therefore, it can be used to select the best ionic liquid ammonia absorbent accurately and efficiently.