Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm.
J Hazard Mater
; 473: 134629, 2024 Jul 15.
Article
de En
| MEDLINE
| ID: mdl-38762987
ABSTRACT
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 â, 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
J Hazard Mater
Sujet du journal:
SAUDE AMBIENTAL
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
Pays-Bas