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Bayesian predictive modeling for gas purification using breakthrough curves
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DOI：10.1016/j.jhazmat.2024.134311.png)
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摘要
En 中文
This study proposes a predictive model for assessing adsorber performance in gas purification processes, specifically targeting the removal of chemical warfare agents (CWAs) using breakthrough curve analysis. Conventional parameter estimation methods, such as Brunauer-Emmett-Teller analysis, encounter challenges due to the limited availability of kinetic and equilibrium data for CWAs. To overcome these challenges, we implement a Bayesian parametric inference method, facilitating direct parameter estimation from breakthrough curves. The model ' s efficacy is confirmed by applying it to H 2 S purification in a fixed -bed setup, where predicted breakthrough curves aligned closely with previous experimental and numerical studies. Furthermore, the model is applied to sarin with ASZM-TEDA carbon, estimating key parameters that could not be assessed through conventional experimental techniques. The reconstructed breakthrough curves closely match actual measurements, highlighting the model ' s accuracy and robustness. This study not only enhances filter performance prediction for CWAs but also offers a streamlined approach for evaluating gas purification technologies under limited experimental data conditions.
Keyword:
Chemical warfare agents
Gas adsorption
Adsorption dynamics
Filter performance
Parameter identification
期刊
IF:
11.3
论文数:
4.0W
被引数:
24.0W
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