Abstract
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.
Highlights
The utilization of environmentally friendly and green energy has been accelerated because of the pollution crisis and increasing global energy demand
The adaptive neuro-fuzzy inference system (ANFIS) algorithm was combined with genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and BP to estimate the diffusivity coefficient in terms of temperature, pressure, and viscosity
The determined R2 values in the testing phase were 0.997753, 0.993202, 0.985409, 0.973849 and 0.951371 for ANFIS optimized by PSO, GA, ACO, BP, and DE respectively
Summary
The utilization of environmentally friendly and green energy has been accelerated because of the pollution crisis and increasing global energy demand. The utilization of carbon dioxide for heat transmission is more applicable for extraction heat from hot fractured rock respect to water (Cui et al, 2016; Pruess, 2006; Zhang et al, 2016). When carbon dioxide has contact with water interface it can diffuse through the water so the diffusion coefficient is known as a major parameter which effects fluid diffusivity(Farajzadeh et al, 2009; Mutoru et al, 2011). This factor has a dominant effect on chemical reactions and mass transfer in porous media and solutions(S. P. Cadogan et al, 2014a; Trevisan et al, 2014b)
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