Abstract Considering the intrinsic difficulty of accurately determining the thermodynamic data of complex impurity‐containing systems in industrial crystallization processes, this study creatively establishes a predictive correlation between infrared spectra, operating conditions, and the thermodynamics of the racemic methionine ( dl ‐methionine) crystallization system in K 2 CO 3 aqueous systems. Using principal component analysis, random forest, and Gaussian process regression architecture, a concentration prediction machine learning sub‐model was constructed, yielding a coefficient of determination of 0.988 in the test set. Subsequently, a first‐principle sub‐model combining equilibrium conditions with the electrolyte activity coefficient model was established by developing a parameter optimization program fitted experimental data to obtain a predicting error of 2.09%. The solubilization effect of carbonate salt on methionine was quantitatively characterized. The influence of temperature, pH, and carbonate component on the distribution and activity coefficients of dl ‐methionine species was thoroughly investigated. The hybrid model can better serve for the analysis of industrial dl ‐methionine crystallization processes.
Ma et al. (Sun,) studied this question.