Prediction of solubility of solid compounds in supercritical CO2 using a connectionist smart technique

In this communication, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed for estimation of solubility of several solid species in supercritical CO2. A total of 795 data points were collected from the literature and used in the process of model development and evaluation. The Genetic Algorithm (GA) was implemented to optimize the radius of influence of initial Fuzzy Inference System (FIS). The tuning parameters of FIS structure were optimized by utilizing a new method based on conjugation of hybrid and Particle Swarm Optimization (PSO) method namely CHPSO method. The accuracy of the proposed model was evaluated using graphical and statistical analysis and by comparing the results with another model reported in the literature. Results show that the developed conjugate ANFIS model is robust and precise in prediction of experimental data and is superior to other literature model. (C) 2016 Elsevier B.V. All rights reserved.