Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor.
A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.
Kim
Y J
YJ
Department of Environmental Engineering, Pusan National University, Busan 609-735, Korea. yjkim@pusan.ac.kr
Bae
H
H
Ko
J H
JH
Poo
K M
KM
Kim
S
S
Kim
C W
CW
Woo
H J
HJ
D000465:Algorithms; D000641:Ammonia / Q000032:analysis; D019149:Bioreactors; D003663:Decision Trees; D017143:Fuzzy Logic; D009584:Nitrogen / Q000032:analysis; D010100:Oxygen / Q000032:analysis / Q000737:chemistry; D014865:Waste Disposal, Fluid
D000641:Ammonia; D009584:Nitrogen; D010100:Oxygen