Accurate estimation of CO 2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm-Reference-Cited by-同舟云学术

Accurate estimation of CO 2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm

Published:2018-03 Issue:1 Volume:27 Page:65-73
ISSN:1110-0621
Container-title:Egyptian Journal of Petroleum
language:en
Short-container-title:Egyptian Journal of Petroleum

Author:

Rostami Alireza,Anbaz Mohammad Amin,Erfani Gahrooei Hamid Reza,Arabloo Milad,Bahadori Alireza

Publisher

Elsevier BV

Subject

Organic Chemistry,Process Chemistry and Technology,Geochemistry and Petrology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Catalysis

Reference42 articles.

1. High temperature capture of CO2 on Li4SiO4-based sorbents from biomass ashes;Wang;Environ. Prog. Sustainable Energy,2015

2. An overview of landfill leachate treatment via activated carbon adsorption process;Foo;J. Hazard. Mater.,2009

3. Adsorption of carbon dioxide using impregnated activated carbon promoted by Zinc;Somy;Int. J. Greenhouse Gas Control,2009

4. Adsorption of carbon dioxide on alkali-modified zeolite 13X adsorbents;Zhao;Int. J. Greenhouse Gas Control,2007

5. Influence of preparation conditions on the characteristics of activated carbons produced in laboratory and pilot scale systems;El Qada;Chem. Eng. J.,2008

Cited by 60 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development of long short-term memory along with differential optimization and neural networks for coagulant dosage prediction in water treatment plant;Journal of Water Process Engineering;2024-08

2. Machine learning methods for predicting CO2 solubility in hydrocarbons;Petroleum Science;2024-05

3. Development of a radiation shield for atmospheric temperature measurement system;Measurement;2024-04

4. Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine learning techniques;Fuel;2024-03

5. Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms;Scientific Reports;2023-12-02