Advanced techniques for the capturing and separation of CO2 – A review

Odunlami, O. A.; Vershima, Denen A.; Oladimeji, T.E.; Nkongho, S.; Ogunlade, S.K.; fakinle, B.S.

doi:10.1016/j.rineng.2022.100512

Cited by 58 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following research steps are described with a flowchart scheme as shown in Figure 1. Population is a collection of data that identifies phenomena [18]. A population with a certain number is called a finite population while a population that has an infinite number is called an infinite population [19].…”

Section: Methodsmentioning

confidence: 99%

Analysis of E-learning user Acceptance using the Technology Acceptance Model (TAM) and end-User Computing Satisfaction (EUCS)

Alamsyah,

Budiman,

Titan Parama

2023

FJAS

View full text Add to dashboard Cite

This research aims to analyse the acceptance of E-Learning among students of Universitas Informatika dan Bisnis Indonesia by using Technology Acceptance Model (TAM) and End-User Computing Satisfaction (EUCS) approaches, which are often used by researchers to examine user acceptance and satisfaction with technology. The results of this study indicate that the influence between the variables Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using, Content, Accuracy, Format, Timeliness, Ease and User Satisfaction obtained a total value of 29,140 and on the continuum line shows that it is in the Very Good category. The results in this study also prove that there are 6 (six) accepted hypotheses and 3 (three) hypotheses that are not accepted. This result can be used as a suggestion for E-Learning Universitas Informatika dan Bisnis Indonesia to evaluate shortcomings in user satisfaction, as well as maintaining existing quality.

show abstract

Section: Methodsmentioning

confidence: 99%

Analysis of E-learning user Acceptance using the Technology Acceptance Model (TAM) and end-User Computing Satisfaction (EUCS)

Alamsyah,

Budiman,

Titan Parama

2023

FJAS

View full text Add to dashboard Cite

show abstract

“…This research focuses on finding suitable polymers for the CO 2 /CH 4 and CO 2 /N 2 gas separations using membranes. These separations pose a considerable challenge due to their similar molecular sizes, physical properties, and low reactivity. , Polymers for CO 2 /CH 4 separation are widely used in industrial processes such as biogas sweetening, natural gas purification, oil production enhancement, and landfill gas cleaning. − CO 2 /N 2 separation polymers are desirable in postcombustion carbon capture technologies, as membrane-based carbon dioxide separation processes offer low energy consumption, ease of operation, and compact design. − …”

Section: Introductionmentioning

confidence: 99%

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

Tiwari,

Shi,

Budhathoki

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO 2 /CH 4 and CO 2 /N 2 gas separation using membranes. Several polymers of interest were identified. The results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.

show abstract

“…Gas turbine (GT) power plants generate exhaust gases apart from electricity production. The flue gas emitted into the power plant's surroundings contains carbon dioxide and other gases which pollute the air and contribute to GHG [ 21 ]. Saravanamutto et al [ 22 ] stated in their book that a large amount of excess air is needed in gas turbine combustible engines, which leads to considerable oxygen in the exhaust gases.…”

Section: Introductionmentioning

confidence: 99%

A novel empirical model for predicting the carbon dioxide emission of a gas turbine power plant

Egware

Kwasi-Effah

2023

Heliyon

View full text Add to dashboard Cite

Advanced techniques for the capturing and separation of CO2 – A review

Cited by 58 publications

References 16 publications

Analysis of E-learning user Acceptance using the Technology Acceptance Model (TAM) and end-User Computing Satisfaction (EUCS)

Analysis of E-learning user Acceptance using the Technology Acceptance Model (TAM) and end-User Computing Satisfaction (EUCS)

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A novel empirical model for predicting the carbon dioxide emission of a gas turbine power plant

Contact Info

Product

Resources

About