Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network

Biofiltration is an economical and environmentally friendly process to eliminate air pollutants. Results obtained by different authors showed the enhanced performance of the fungal biofiltering systems. Consequently, there is a necessity to develop methodologies not only to design more efficient reactors but to control the reaction behavior under different conditions: pollutants feeding, air flows, humidity and biomass production. In this study, a continuous neural network observer was designed to predict the toluene vapors elimination capacity (EC) in a fungal biofilter. The observer uses the carbon dioxide (CO₂) production and the pressure drop (DP) (on line measurements) as input information. The differential neural network observer proved to be a useful tool to reconstruct the immeasurable on-line variable (EC). The observer was successfully tested under different reaction conditions proving the robustness of estimation process. This software sensor may be helpful to derive adaptive control functions optimizing the biofilter reaction development.