Biofiltration is an efficient and economically viable air pollution control technique, wherein gas phase pollutants (industrial emissions) are biodegraded to innocuous end-products such as carbon dioxide, water, in a filter matrix that contains a thriving microbial consortium.
Published in Chapter:
ANNs for Identifying Shock Loads in Continuously Operated Biofilters: Application to Biological Waste Gas Treatment
Eldon R. Rene (University of La Coruña, Spain), M. Estefanía López (University of La Coruña, Spain), Hung Suck Park (University of Ulsan, South Korea), D. V. S. Murthy (Broward College, USA), and T. Swaminathan (Indian Institute of Technology, India)
Copyright: © 2012
|Pages: 32
DOI: 10.4018/978-1-4666-0294-6.ch004
Abstract
Among the different waste gas treatment techniques developed to eliminate odorous and toxic pollutants from air, biological techniques have emerged as an effective, reliable, eco-friendly, simple, and economical option. Biological waste gas treatment systems such as biofilters are commonly used in industrial complexes to handle emissions at high gas flow rates and low pollutant concentrations (<5 g/m3). However, from a practical view-point, variation in concentrations and gas flow rates are common to any industrial emission, and it is a pre-requisite to simulate these conditions (shock loads) at the laboratory scale. This chapter provides sufficient theoretical background information on the different waste gas treatment systems, literature review on shock loads in biofilters, and the different steady and transient state models developed in the field of biofiltration. A fundamental overview of artificial neural networks and the different steps of the modeling process are also presented.