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

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.表征传质过程的重要参数之一为二氧化碳扩散系数，其对海洋生态系统、咸水含水层以及枯竭储层中的二氧化碳封存具有显著影响。由于二氧化碳-盐水体系扩散系数的测定需要复杂的解释方法与专用实验设备，因此计算与数学方法更受青睐。本文将自适应神经模糊推理系统（Adaptive Neuro-Fuzzy Inference System，ANFIS）与五种不同的进化算法相结合，用于预测二氧化碳扩散系数。经粒子群优化（Particle Swarm Optimization，PSO）、遗传算法（Genetic Algorithms，GA）、蚁群优化（Ant Colony Optimization，ACO）、反向传播（Backpropagation，BP）与差分进化（Differential Evolution，DE）优化的ANFIS模型，其测试阶段的决定系数R²值分别为0.9978、0.9932、0.9854、0.9738与0.9514。其中ANFIS-PSO混合机器学习模型的性能优于其余所有模型。