“…According to past publications, carbonation models can be classified into four categories based on how the relationship between carbonation depth and its influencing parameters is determined [1]: (1) empirical models, where the relationship between the carbonation depth and its determinants is derived from actual experiments [2][3][4][5][6][7][8][9], such as the square root model [3]; (2) statistical models, where the dependent and independent variables are related by mathematical functions, such as multiple linear regression [10]; (3) numerical models, which consider several physiochemical equations, including chemical reaction rates, mass conservation in gas-liquid two-phase flow, diffusion and dispersion of CO2 in water, energy conservation in porous media, and solubility of CO2 in water, and simulate the phenomena by computer software [11][12][13][14]; (4) machine learning (ML)-based models, which has been applied in recent simulations to find complex nonlinear relationships during the carbonation process [15][16][17][18]. However, all these models have their drawbacks.…”