Artificial neural network modeling for Congo red adsorption on microwave-synthesized akaganeite nanoparticles: optimization, kinetics, mechanism, and thermodynamics

Autor:	Vinh D, Nguyen, Hoa T H, Nguyen, Valerie, Vranova, Linh T N, Nguyen, Quy M, Bui, Tam T, Khieu
Rok vydání:	2020
Předmět:	Kinetics Nanoparticles Thermodynamics Congo Red Adsorption Neural Networks Computer Hydrogen-Ion Concentration Microwaves Ferric Compounds Water Pollutants Chemical
Zdroj:	Environmental science and pollution research international. 28(8)
ISSN:	1614-7499
Popis:	This work aims to synthesize akaganeite nanoparticles (AKNPs) by using microwave and use them to adsorb Congo red dye (CR) from the aqueous solution. The AKNPs with an average particle size of about 50 nm in width and 100 nm in length could be fabricated in 20 min. The effects of pH, CR initial concentration, adsorption time, and adsorbent dosage on the adsorption process were investigated and the artificial neural network (ANN) was used to analyze the adsorption data. The various ANN structures were examined in training the data to find the optimal model. The structure with training function, TRAINLM; adaptation learning function, LARNGDM; transfer function, LOGSIG (in hidden layer) and PURELIN (in output layer); and 10 neutrons in hidden layer having the highest correlation (R
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::cc6403a628c05973605e43ade09e40a1 https://pubmed.ncbi.nlm.nih.gov/33128712 Zobrazit plný text záznamu Full text from SpringerLink