Modeling of bio filters and bio trickling filters for odor and VOC control applications
To Modeling Biological treatment of contaminated air in bio filters and bio trickling filters involves a complex combination of physical, chemical, and biological processes. This makes it very difficult to mentally understand and integrate the subtle details of the phenomena occurring during treatment. Even simple exercises, such as to predict the behavior of bio filters and bio trickling filters under a different set of conditions, can be challenging. Fortunately, computers and mathematical models can track a multitude of complex relationships much better than the human mind. Therefore, mathematical models can be very useful tools in research and for design. Mathematical models help in the development of a fundamental understanding of the process, and to accomplish engineering tasks such as reactor design and scale up, or process optimization. There are many advantages to modeling and simulation, including: • The ability to gain insight into a given process. • The possibility to obtain quantitative information on a variable that is difficult or impossible to measure (e.g., concentration of contaminant in the bio film). • The low cost and rapidity of performing "virtual experiments". • The ability to evaluate conditions that may not be possible to test. • The possible use in experimental design in order to maximize outcome and representative ness of the experimental data. • The ability to automatically perform numerous simulations to support process optimization. At the same time, one has to acknowledge the many limitations of mathematical modeling: • Models will only be as good as the basic concept and the assumptions that were made during model development. • In many instances, model parameters will be unknown, and it will require significant experimentation to determine these model parameters. • The fact that simply because a model fits the experimental data does not mean that the model or the concept on which the model is based are correct. • Application of a model and extrapolation of modeling data outside the range for which the model has been validated, or application of a model for a completely different system involve risks, as uncertainties and discrepancies may be multiplied. Models may be classified depending on their features and structures. In this chapter, the focus was placed on models that make attempts to describe the actual phenomena occurring during a biological treatment. These are often called structured or conceptual models. A number of other models, ranging from simple multi-parameter correlations to lumped parameter models, exist but these are not discussed in great detail here. © Springer-Verlag Berlin Heidelberg 2005.
