Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

• Main Authors: Amin Bemani, Alireza Baghban, Amir Mosavi, Shahab S.
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: Engineering Applications of Computational Fluid Mechanics
• Subjects: carbon dioxide; co2; anfis; prediction; forecasting; diffusion coefficient; brine systems; evolutionary algorithm; hydro environmental informatics; hydro-informatics; machine learning
• Online Access: http://dx.doi.org/10.1080/19942060.2020.1774422
• ISSN: 1994-2060; 1997-003X

One of the important parameters illustrating the mass transfer process is the diffusion coefficient of carbon dioxide which has a great impact on carbon dioxide storage in marine ecosystems, saline aquifers, and depleted reservoirs. Due to the complex interpretation approaches and special laboratory equipment for measurement of carbon dioxide-brine system diffusivity, the computational and mathematical methods are preferred. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is coupled with five different evolutionary algorithms for predicting the diffusivity coefficient of carbon dioxide. The R2 values forthe testing phase are 0.9978, 0.9932, 0.9854, 0.9738 and 0.9514 for ANFIS optimized by particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), backpropagation (BP), and differential evolution (DE), respectively. The hybrid machine learning model of ANFIS-PSO outperforms the other models.