Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.
A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.
Perendeci
Altinay
A
Environmental Engineering Department, Akdeniz University, Antalya, Turkey. aperendeci@akdeniz.edu.tr
Arslan
Sever
S
Tanyolaç
Abdurrahman
A
Celebi
Serdar S
SS
D000693:Anaerobiosis; D017143:Fuzzy Logic; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D014865:Waste Disposal, Fluid / Q000379:methods*; D018508:Water Purification / Q000379:methods*