Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
Abstract In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted...
Enregistré dans:
Auteurs principaux: | Mohammad Hadi Dehghani, Amir Hessam Hassani, Rama Rao Karri, Bahareh Younesi, Mansoureh Shayeghi, Mehdi Salari, Ahmad Zarei, Mahmood Yousefi, Zoha Heidarinejad |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/c9d7d6913d274c48953b6aa37b86e02b |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach
par: Majedeh Gheytanzadeh, et autres
Publié: (2021) -
Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
par: Mohammad Hadi Dehghani, et autres
Publié: (2021) -
OF TRANSIENT MASS TRANSFER OF A GASEOUS COMPONENT IN AN ISOTHERMAL POROUS ADSORBENT
par: Pétrissans,Anélie, et autres
Publié: (2010) -
Estimating equivalence scales and non-food needs in Egypt: Parametric and semiparametric regression modeling.
par: Fuad A Awwad, et autres
Publié: (2021) -
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models
par: Ping Zeng, et autres
Publié: (2017)