Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm.

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g−1 (25 ℃, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents. [Display omitted] • Adsorption mechanism of ZIF-76 on dyes mainly depends on π-π stacking interaction. • Python-based ANN accurately predicts ZIF-67 adsorption of malachite green. • The model consists of 12 neuron, L-BFGS algorithm, and Tanh activation function. • Systematically explores the interplay of preparation, structure, and adsorption. • Python-based ANN outperforms Matlab in predictive accuracy and generalization. [ABSTRACT FROM AUTHOR]