Int. J. Computer Applications in Technology, Vol. 65, No. 1, 2021 1 Copyright © 2021 Inderscience Enterprises Ltd. Determining solubility of CO 2 in aqueous brine systems via hybrid smart strategies Tofigh Sayahi Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Boston MA, USA and Department of Chemical Engineering, University of Utah, Salt Lake City, Utah 84112, USA Email: tofighsayahi@chemeng.utah.edu Afshin Tatar Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran Email: afshin.tatar@gmail.com Alireza Rostami*, Mohammad Amin Anbaz and Khalil Shahbazi Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran Email: alireza.rostami@afp.put.ac.ir Email: alireza.rostami.put2014@gmail.com Email: amin.anbaz@gmail.com Email: shahbazi@put.ac.ir *Corresponding author Abstract: In this study, Radial Basis Function Neural Network (RBF-NN) and Least Square Support Vector Machine (LSSVM) were established for estimation of equilibrium CO 2 -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 CO 2 solubility with determination factor (R 2 ) of 0.9966 and average absolute relative deviation (AARD%) of 2.5885% for COA-LSSVM, and AARD% = 3.8832% and R 2 = 0.9962 for COA-RBF-NN; therefore, the proposed COA- LSSVM gives more accurate results for estimating CO 2 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. Keywords: equilibrium CO 2 -water/brine; solubility; least squares support vector machine; carbon capture and storage; outliers analysis; radial basis function neural network. Reference to this paper should be made as follows: Sayahi, T., Tatar, A., Rostami, A., Anbaz, M.A. and Shahbazi, K. (2021) ‘Determining solubility of CO 2 in aqueous brine systems via hybrid smart strategies’, Int. J. Computer Applications in Technology, Vol. 65, No. 1, pp.1–13. Biographical notes: Tofigh Sayahi received his PhD degree from University of Utah focussing on air quality control. He has published several papers in the field of chemical engineering, oil and gas science and data mining. Afshin Tatar is skilled in Artificial Intelligence, Machine Learning, Project Management and Research and Development (R&D). He is a Strong Professional Engineer with a Master of Science (MSc) focused on applications of Artificial Intelligence in Chemical Engineering.