Mathematical Modeling of Deep-Bed Biofiltration to Describe Contaminant Control and Headloss
Last Modified: Aug 10, 2018
- Wendell Khunjar, Erik Rosenfeldt, Meric Selbes - Hazen and Sawyer
- Yewei Sun, Zhi-Wu Wang - Occoquan Laboratory, Department of Civil and Environmental Engineering, Virginia Tech
Deep-bed biofiltration technology has been broadly applied in water industry for decades. Biofiltration can effectively remove soluble and particulate organics and nutrients using biofilms and their supporting media (e.g., granular activated carbon) through particle deposition, adsorption and biodegradation. During operation, biofilm growth and particle deposition will increase headloss across the filter, which can negatively impact contaminants removal. Periodic backwash of the biofilter is typically performed in response to headloss buildup. At many full-scale facilities, backwash has been a significant energy and maintenance burden. Thus, there is a need to develop strategies that optimize filter design and operation with respect to backwash requirements. However, the industry still currently lacks a systematic tool for helping biofilter design and optimization. In this study, we developed a biofiltration process kinetic model to quantify contaminant removal while simultaneously predicting headloss development. This model not only considered particle deposition and adsorption on filtration media, but also the biofilm growth during biodegradation of contaminants. The model includes inputs like flowrate, temperature, influent contaminants including organics (e.g., TOC, DOC or COD), nutrients (e.g., N, P), and suspended solids (TSS or turbidity), and can predict the headloss as well as contaminants profile along biofiltration depth and time.
Application of the model to full-scale biofilter data was performed. Analyses indicated that the contributions of particle deposition on headloss accumulation were negligible in this system whose biofilter influent had low particulate content. Instead, biofilm growth was the key contributor to headloss accumulation in this system. Moreover, it was found that contaminants breakthrough could be attributed to the reduced hydraulic retention time caused by bed porosity decrease as a result of biofilm growth. The outcome of this study will shed light on prediction and optimization of headloss accumulation as well as contaminant control in deep-bed biofiltration for water and advanced wastewater treatment.
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