Learning Bayesian networks based diagnosis system for wastewater treatment process with sensor data.
Contemporary technical capabilities allow an operator to easily monitor and control several remote wastewater treatment processes simultaneously but an on-line automatic diagnostic system has not yet been installed. In this paper, an on-line diagnostic system is proposed, designed and implemented for the lab-scale five-stage step-feed Enhanced Biological Phosphorus Removal plant based upon a learning Bayesian network. In order to practically diagnose wastewater treatment processes, a lab-scale pilot plant was built and the proposed on-line diagnostic method was applied to evaluate the performance of the algorithm. In experimental results, real abnormal conditions occurred 21 times in a three month period. The suggested on-line diagnosis system made correct predictions 14 times and incorrect predictions 7 times. Moreover, a comparison of the prediction results of the Bayesian model and learning Bayesian model clearly show that learning algorithm became more effective as time passed.
Cheon
Seong-Pyo
SP
School of Electrical and Computer Engineering, Pusan National University, Busan, South Korea.
Kim
Sungshin
S
Kim
Jongrack
J
Kim
Changwon
C
D001499:Bayes Theorem; D015374:Biosensing Techniques; D007753:Laboratories; D009566:Nitrates / Q000032:analysis; D010758:Phosphorus / Q000302:isolation & purification; D013997:Time Factors; D014867:Water / Q000592:standards; D018508:Water Purification / Q000706:statistics & numerical data*
D009566:Nitrates; D014867:Water; D010758:Phosphorus