Improving decision making in water plant operability through Bayesian Belief Networks

摘要

Real-time process and water quality monitoring has improved compliance and risk reduction in water treatment plants; however, it is a challenge to manage and extract maximum value from the terabytes of data generated by on-line instruments. This study used Bayesian Belief Network (BBN) to expand the use of historical data for improving decision making in water treatment plant operations. BBNs were developed and validated using on-line turbidity data and related operational conditions at the Mount Pleasant Filtration plant in South Australia. Data was converted to probability functions for possible causes and corresponding corrective actions conditions of high turbidity at the filter outlet. This quantitative statistical information can be used to develop appropriate response to “out of normal” operation events, e.g. events that cause turbidity excursions and other noncompliant conditions during operation.