title: Learning Bayesian networks based diagnosis system for wastewater treatment process with sensor data.
abstract: 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.
authors.affiliation: School of Electrical and Computer Engineering, Pusan National University, Busan, South Korea.
authors.forename: Seong-Pyo
authors.initials: SP
authors.lastname: Cheon
authors.forename: Sungshin
authors.initials: S
authors.lastname: Kim
authors.forename: Jongrack
authors.initials: J
authors.lastname: Kim
authors.forename: Changwon
authors.initials: C
authors.lastname: Kim
mesh_terms: 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*
chemical_list: D009566:Nitrates; D014867:Water; D010758:Phosphorus