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.

说明传质过程的重要参数之一是二氧化碳的扩散系数，该系数对海洋生态系统，盐水层和枯竭的水库中的二氧化碳存储有很大影响。由于复杂的解释方法和用于测量二氧化碳-盐水系统扩散率的专用实验室设备，因此首选计算和数学方法。在本文中，自适应神经模糊推理系统（ANFIS）与五种不同的进化算法相结合，用于预测二氧化碳的扩散系数。R ²通过粒子群优化（PSO），遗传算法（GA），蚁群优化（ACO），反向传播（BP）和差异进化（DE）优化的ANFIS，测试阶段的值分别为0.9978、0.9932、0.9854、0.9738和0.9514分别。ANFIS-PSO的混合机器学习模型优于其他模型。