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Research Article

Experimental validation of adsorption filter model under dynamic VOC concentrations: Prediction of long-term efficiency

Ruiyan Zhang1Ziying Li1Xiangyuan Guan1Xin Wang1Fei Wang1Lingjie Zeng2( )Zhenhai Li2
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
School of Mechanical Engineering, Tongji University, Shanghai 200092, China
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Abstract

Indoor volatile organic compound (VOC) concentrations are often dynamic because the ventilation and emission rates of VOC usually change. Adsorption filters used for air purification may operate with a capacity that fluctuates with unsteady VOC concentrations in buildings. Modeling the dynamic interactions between adsorption filters and indoor air is crucial for predicting their performance under real-world conditions. This study presents a numerical model of partially reversible adsorption equilibrium coupled with a mass transfer model to create a predictive model for adsorption efficiency in environments with dynamic VOC concentrations. A honeycomb adsorption filter for benzene adsorption was simulated and tested, including the breakthrough and purging curve and the long-term efficiency in an experimental chamber with dynamic concentrations. The results reveal that the curve generated with the partially reversible adsorption equilibrium model closely aligns with the measured one. Furthermore, the model was coupled with a chamber model and the simulation results were compared with those calculated using the filter model with a single adsorption isotherm. When VOCs were emitted intermittently in the chamber and there was sufficient ventilation, the concentration peaks in the chamber derived from the models with different assumptions on adsorption reversibility were significantly different from each other. Moreover, it was observed that the reversible adsorption capacity of the filter was crucial for long-term operation in rooms with dynamic concentration. Despite the reversible adsorption capacity constituting only 6.7% of the total adsorption capacity of the tested filter, it contributes to a significant “peak shaving and valley filling” effect, even when the irreversible adsorption capacity is saturated. The adsorption reversibility should be taken as an important parameter for selecting adsorbents for dynamic concentration conditions.

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References

 

Aguirre-Contreras S, Leyva-Ramos R, Ocampo-Pérez R, et al. (2023). Mathematical modeling of breakthrough curves for 8-hydroxyquinoline removal from fundamental equilibrium and adsorption rate studies. Journal of Water Process Engineering, 54: 103967.

 

Axley JW (1991). Adsorption modelling for building contaminant dispersal analysis. Indoor Air, 1: 147–171.

 
Axley J, Lorenzetti D (1993). Sorption transport models for indoor air quality analysis. ASTM Selected Technical Papers, available at https://doi.org/10.1520/STP13102S
 

Bale S, Sathe M, Ayeni O, et al. (2017). Spatially resolved mass transfer coefficient for moderate Reynolds number flows in packed beds: Wall effects. International Journal of Heat and Mass Transfer, 110: 406–415.

 

Bellat JP, Bezverkhyy I, Weber G, et al. (2015). Capture of formaldehyde by adsorption on nanoporous materials. Journal of Hazardous Materials, 300: 711–717.

 

Chu KH (2020). Breakthrough curve analysis by simplistic models of fixed bed adsorption: In defense of the century-old Bohart-Adams model. Chemical Engineering Journal, 380: 122513.

 

Das D, Gaur V, Verma N (2004). Removal of volatile organic compound by activated carbon fiber. Carbon, 42: 2949–2962.

 

Di Toro DM, Horzempa LM (1982). Reversible and resistant components of PCB adsorption-desorption: Isotherms. Environmental Science & Technology, 16: 594–602.

 

Díaz-Blancas V, Aguilar-Madera C, Flores-Cano J, et al. (2020). Evaluation of mass transfer mechanisms involved during the adsorption of metronidazole on granular activated carbon in fixed bed column. Journal of Water Process Engineering, 36: 101303.

 

Franco DSP, Fagundes JLS, Georgin J, et al. (2020). A mass transfer study considering intraparticle diffusion and axial dispersion for fixed-bed adsorption of crystal violet on pecan pericarp (Carya illinoensis). Chemical Engineering Journal, 397: 125423.

 

Grande C (2022). Modelling of adsorption technologies for controlling indoor air quality. Adsorption, 28: 1–13.

 

He C, Chen W, Han K, et al. (2014). Evaluation of filter media performance: correlation between high and low challenge concentration tests for toluene and formaldehyde (ASHRAE RP-1557). HVAC&R Research, 20: 508–521.

 

Hu Y, Xu L, Liang W (2023). A preliminary study on volatile organic compounds and odor in university dormitories: Situation, contribution, and correlation. Building Simulation, 16: 379–391.

 

Kan AT, Fu G, Tomson MB (1994). Adsorption/Desorption hysteresis in organic pollutant and soil/sediment interaction. Environmental Science & Technology, 28: 859–867.

 

Khararoodi MG, Lee CS, Haghighat F (2022). Modelling of sorbent-based gas filters for indoor environment: A comprehensive review. Building and Environment, 208: 108579.

 

Khararoodi MG, Haghighat F, Lee CS (2023). Develop and validate a mathematical model to estimate the removal of indoor VOCs by carbon filters. Building and Environment, 233: 110082.

 

Kosuge K, Kubo S, Kikukawa N, et al. (2007). Effect of pore structure in mesoporous silicas on VOC dynamic adsorption/desorption performance. Langmuir, 23: 3095–3102.

 

Li X, Zhang L, Yang Z, et al. (2020). Adsorption materials for volatile organic compounds (VOCs) and the key factors for VOCs adsorption process: A review. Separation and Purification Technology, 235: 116213.

 

Liu X, Gan J, Zhong W, et al. (2020). Particle shape effects on dynamic behaviors in a spouted bed: CFD-DEM study. Powder Technology, 361: 349–362.

 

Lu L, Huang X, Zhou X, et al. (2024). High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning. Building Simulation, 17: 415–429.

 

Maximoff SN, Mittal R, Kaushik A, et al. (2022). Performance evaluation of activated carbon sorbents for indoor air purification during normal and wildfire events. Chemosphere, 304: 135314.

 

Moghaddam EM, Foumeny EA, Stankiewicz AI, et al. (2021). Multiscale modelling of wall-to-bed heat transfer in fixed beds with non-spherical pellets: From particle-resolved CFD to pseudo-homogenous models. Chemical Engineering Science, 236: 116532.

 

Pei J, Zhang J (2010). Modeling of sorbent-based gas filters: Development, verification and experimental validation. Building Simulation, 3: 75–86.

 

Pei J, Zhang J (2012). Determination of adsorption isotherm and diffusion coefficient of toluene on activated carbon at low concentrations. Building and Environment, 48: 66–76.

 
Popescu RS, Blondeau P, Jouandon E, et al. (2007). Breakthrough time of activated-carbon filters used in residential and office buildings—Modelling and comparison with experimental data. In: Proceedings of the REHVA World Congress CLIMA (2007), Helsinki, Finland.
 

Popescu RS, Blondeau P, Jouandon E, et al. (2013). Elemental modeling of adsorption filter efficiency for indoor air quality applications. Building and Environment, 66: 11–22.

 

Ranz WE, Marshall WR (1952). Evaporation from drops, part Ⅰ. Chemical Engineering Progress, 48: 141–146.

 

Sang L, Feng X, Tu J, et al. (2020). Investigation of external mass transfer in micropacked bed reactors. Chemical Engineering Journal, 393: 124793.

 

Shaverdi G, Haghighat F, Ghaly W (2014). Development and systematic validation of an adsorption filter model. Building and Environment, 73: 64–74.

 

Vizhemehr AK, Haghighat F, Lee CS (2013). Predicting gas-phase air-cleaning system efficiency at low concentration using high concentration results: Development of a framework. Building and Environment, 68: 12–21.

 

Wakao N, Funazkri T (1978). Effect of fluid dispersion coefficients on particle-to-fluid mass transfer coefficients in packed beds: correlation of Sherwood numbers. Chemical Engineering Science, 33: 1375–1384.

 
Wood GO (2000). Reviews of models for adsorption of single vapors, mixtures of vapors, and vapors at high humidities on activated carbon for applications including predicting service lives of organic vapor respirator cartridges. Los Alamos National Laboratory LA-UR-00-1531.
 

Wu F, Dong H, Yu C, et al. (2024). Numerical simulation of formaldehyde distribution characteristics in the high-speed train cabin. Building Simulation, 17: 285–300.

 

Yao M, Zhang Q, Hand D, et al. (2009). Modeling of adsorption and regeneration of volatile organic compounds on activated carbon fiber cloth. Journal of Environmental Engineering, 135: 1371–1379.

 

Yue X, Ma NL, Sonne C, et al. (2021). Mitigation of indoor air pollution: A review of recent advances in adsorption materials and catalytic oxidation. Journal of Hazardous Materials, 405: 124138.

 

Zhang R, Li Z, Zeng L (2020). Experimental study of the relationship between initial single-pass efficiency and structural parameters of honeycomb adsorption filters. Building and Environment, 186: 107332.

 

Zhang R, Zeng L, Wang F, et al. (2022). Influence of pore volume and surface area on benzene adsorption capacity of activated carbons in indoor environments. Building and Environment, 216: 109011.

 

Zhang R, Li Z, Wang X, et al. (2023). Adsorption equilibrium of activated carbon amid fluctuating benzene concentration in indoor environments. Building and Environment, 245: 110964.

Building Simulation
Pages 1201-1212
Cite this article:
Zhang R, Li Z, Guan X, et al. Experimental validation of adsorption filter model under dynamic VOC concentrations: Prediction of long-term efficiency. Building Simulation, 2024, 17(7): 1201-1212. https://doi.org/10.1007/s12273-024-1135-4

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Received: 01 February 2024
Revised: 03 April 2024
Accepted: 07 April 2024
Published: 20 June 2024
© Tsinghua University Press 2024
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