Determining solubility of CO2 in aqueous brine systems via hybrid smart strategies

Abstract

In this study, Radial Basis Function Neural Network (RBF-NN) and Least Square Support Vector Machine (LSSVM) were established for estimation of equilibrium CO2-water/brine solubility as a function of salt molecular weight, temperature, salt molality and pressure. A reliable database was gathered from the open source literatures, and was split into two groups of testing and training subsets. Optimal structure of the proposed RBF-NN technique and the tuning coefficients of LSSVM model were determined by Cuckoo Optimisation Algorithm (COA). Accordingly, the proposed approaches here can accurately prognosticate CO2 solubility with determination factor (R2) of 0.9966 and average absolute relative deviation (AARD%) of 2.5885% for COA-LSSVM, and AARD% = 3.8832% and R2 = 0.9962 for COA-RBF-NN; therefore, the proposed COA-LSSVM gives more accurate results for estimating CO2 solubility. Williams’ outliers detection technique reveals that less than 3% of database are outliers. Salt molality is the most affecting variable based on sensitivity analysis.