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This paper presents the development of two models for simulating residual chlorine decay in raw and treated waters collected from six different utilities of the Quebec City area. The data for modelling was generated by means of several bench-scale chlorination assays undertaken with chlorine doses that varied according to the content of natural organic matter in the water. The first model is a classical kinetic model in which chlorine decay is represented by a first or a second order function, according to the contact time of chlorine. A decay coefficient is estimated based on water quality and operational parameters, using linear regression. The second is a non-linear back propagation neural network model in which all the parameters responsible for chlorine decay are processed within a single model. The performances of both models were evaluated and compared using the database for model development and an independent database for validation. Both models demonstrated acceptable abilities for simulating residual chlorine decay. However, the back propagation neural network model gave significantly better results for conditions involving high chlorine dosage, high organic matter content and long reaction times during chlorination experiments. In the other cases, the performance of the kinetic model was slightly superior. Back propagation neural networks are also advantageous because they do not require assumptions about model order or the estimation of a chlorine decay coefficient. Also included in the paper is a discussion of possible strategies for use in future research work in order to generalize the results obtained.

Estimation of Bench-Scale Chlorine Decay in Drinking Water Using nth-Order Kinetic and Back Propagation Neural Network Models