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 language neural network model based on 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. The innovative use of Artificial Neural Network (ANN) in predicting and optimizing ZIF-67's adsorption performance under specific synthesis conditions directly contributes to environmental conservation efforts. By tailoring materials with targeted adsorption capacities, this approach significantly reduces environmental degradation caused by contaminants like malachite green. The precise synthesis parameters provided by the intelligent model not only enhance adsorption efficiency but also support sustainable materials design. This contributes to minimizing pressure on the natural ecosystem, reducing health risks associated with contaminants, and promoting the conservation of biodiversity and wildlife.