American Society of Civil Engineers


Application of Artificial Neural Networks for Filtration Optimization


by K. A. Griffiths, (corresponding author), (Graduate student, Dept. of Civil Engineering, Univ. of Toronto, 35 St. George St., Toronto, ON, M5S 1A4, Canada. E-mail: kelly.griffiths@utoronto.ca) and R. C. Andrews, (Professor, Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chairholder, Dept. of Civil Engineering, Univ. of Toronto, 35 St. George St., Toronto, ON, M5S 1A4, Canada. E-mail: andrews@ecf.utoronto.ca)

Journal of Environmental Engineering, Vol. 137, No. 11, November 2011, pp. 1040-1047, (doi:  http://dx.doi.org/10.1061/(ASCE)EE.1943-7870.0000439)

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Document type: Journal Paper
Abstract: Granular media filtration is an important process in drinking water treatment to ensure the adequate removal of particle-bound pathogens (i.e., Giardia and Cryptosporidium). Filtration performance is typically monitored in terms of filtered water turbidity. However, particle counts may provide further insight into treatment efficiency, as they have a greater sensitivity for detecting small changes in filtration operation. Artificial neural networks (ANN) models were applied to optimize filtration at the Elgin Area water treatment plant (WTP) in terms of postfiltration particle counts. Process models were successfully developed to predict postfiltration particle counts. Two inverse-process models were developed to predict the optimal coagulant dosage required to attain target particle counts. Upon testing each model, a high correlation was observed between the actual and predicted data sets. The ANNs were then integrated into an optimization application to allow for the transfer of real-time data between the models and the online supervisory control and data acquisition (SCADA) system.


ASCE Subject Headings:
Filtration
Optimization
Water treatment
Neural networks
Predictions
Particles

Author Keywords:
Filtration
Neural networks
Optimization
Particles
Predictions
Water treatment