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

Authors: Tofigh Sayahi; Afshin Tatar; Alireza Rostami; Mohammad Amin Anbaz; Khalil Shahbazi

Addresses: Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Boston MA, USA; Department of Chemical Engineering, University of Utah, Salt Lake City, Utah 84112, USA ' Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran ' Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran ' Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran ' Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

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.

Keywords: equilibrium CO2-water/brine; solubility; least squares support vector machine; carbon capture and storage; outliers analysis; radial basis function neural network.

DOI: 10.1504/IJCAT.2021.113650

International Journal of Computer Applications in Technology, 2021 Vol.65 No.1, pp.1 - 13

Received: 17 Feb 2020
Accepted: 13 Jul 2020

Published online: 06 Mar 2021 *

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