Normalization of the power-law rate equation for determining the kinetic parameters of the char-CO2 reaction

Geng, Ping; Zheng, Jun; Li, Chongcong; Zhang, Yan

doi:10.1016/j.tca.2021.178933

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The power law serves as a common choice for elucidating the kinetics of chemical reactions. 50 Consequently, the power law equation was deemed to be a suitable initial framework for formulating the degradation rate models in this study. The power law equation was first used to correlate all parameters to the degradation rate measured in terms of the initial degradation rate of amine (mM/h).…”

Section: Degradation Model Development Using Multilinear Regressionmentioning

confidence: 99%

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Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Muchan,

Kruthasoot,

Kongton

et al. 2024

ACS Omega

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In this study, the degradation behavior of 30 different amines was investigated, which were categorized into four distinct groups: alkanolamines, sterically hindered alkanolamines, multialkylamines, and cyclic amines. These experiments were conducted over a period of 14 days at a temperature of 60 °C, with a feed gas comprising 99.9% O 2 flowing at a rate of 200 mL/min. The primary objective was to establish a correlation between the chemical structures of these amines and their susceptibilities to degradation. To assess this, the concentration of the amines at various time points was measured to determine their degradation rates. Results showed that secondary amines exhibited degradation rates higher than those of primary and tertiary amines. Amines with cyclic structures demonstrated lower oxidative degradation rates. Longer alkyl chain lengths decreased degradation rates in all amine types because of their electronic and steric hindrance properties. A higher number of hydroxyl groups increased the degradation rate by destabilizing the free radical. An increase in hydroxyl groups in nonsterically hindered amines increased the degradation rate by decreasing free radical stability. In contrast, for sterically hindered amines, an increase in hydroxyl groups decreased the degradation rate because the steric hindrance effect is now more dominant than the electron-withdrawing effect. An increase in the number of amino groups led to higher degradation rates due to the presence of more reactive sites for free radical formation. Amines with tert-alkyl groups exhibited higher degradation rates than those with straight chains. Moreover, branched alkyl groups located between amino and hydroxyl groups significantly increased the degradation rates. Two degradation models, a semiempirical statistical model and a CatBoost machine learning regression model, were developed to predict amine degradation rates based on their chemical structure and relevant properties. To train these models, a data set of 27 different amines was used, while another set of 3 amines was reserved for testing the model's predictive performance. The average absolute deviations (AAD) achieved were, respectively, 22.2 and 0.3%.

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Section: Degradation Model Development Using Multilinear Regressionmentioning

confidence: 99%

“…The power law serves as a common choice for elucidating the kinetics of chemical reactions . Consequently, the power law equation was deemed to be a suitable initial framework for formulating the degradation rate models in this study.…”

Section: Experimental Sectionmentioning

confidence: 99%

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Muchan,

Kruthasoot,

Kongton

et al. 2024

ACS Omega

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Some new insights into the kinetic compensation effect in different diffusion-controlled domain for char-CO2 gasification

Sun,

Cui,

Zhang

et al. 2023

Renewable Energy

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The effects of Calcium deactivation on the measurement of activation energy in char-CO2 gasification

Geng

Sun²,

Fu³

et al. 2022

Thermochimica Acta

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Normalization of the power-law rate equation for determining the kinetic parameters of the char-CO2 reaction

Cited by 6 publications

References 22 publications

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Some new insights into the kinetic compensation effect in different diffusion-controlled domain for char-CO2 gasification

The effects of Calcium deactivation on the measurement of activation energy in char-CO2 gasification

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Normalization of the power-law rate equation for determining the kinetic parameters of the char-CO2 reaction

Cited by 6 publications

References 22 publications

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO2 Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO2 Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Some new insights into the kinetic compensation effect in different diffusion-controlled domain for char-CO2 gasification

The effects of Calcium deactivation on the measurement of activation energy in char-CO2 gasification

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Product

Resources

About

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO₂ Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches