Neural-fuzzy control system application for monitoring process response andcontrol of anaerobic hybrid reactor in wastewatertreatment and biogas production
Chaiwat Waewsak , Annop Nopharatana , Pawinee Chaiprasert
DOI:
Received November 10, 2009,Revised May 26, 2010, Accepted , Available online
Volume 22,2010,Pages 1883-1890
- Summary
- References
- Related Articles
Based on the developed neural-fuzzy control system for anaerobic hybrid reactor (AHR) in wastewater treatment and biogas
production, the neural network with backpropagation algorithm for prediction of the variables pH, alkalinity (Alk) and total volatile
acids (TVA) at present day time t was used as input data for the fuzzy logic to calculate the influent feed flow rate that was applied
to control and monitor the process response at di erent operations in the initial, overload influent feeding and the recovery phases. In
all three phases, this neural-fuzzy control system showed great potential to control AHR in high stability and performance and quick
response. Although in the overloading operation phase II with two fold calculating influent flow rate together with a two fold organic
loading rate (OLR), this control system had rapid response and was sensitive to the intended overload. When the influent feeding rate
was followed by the calculation of control system in the initial operation phase I and the recovery operation phase III, it was found that
the neural-fuzzy control system application was capable of controlling the AHR in a good manner with the pH close to 7, TVA/Alk <
0.4 and COD removal > 80% with biogas and methane yields at 0.45 and 0.30 m3/kg COD removed.
