Modelling pressure drop in water treatment

Abstract

Industrial concern regarding the use of ‘black box’ models has understandably limited their routine application. In an attempt to overcome some of these concerns the technique of hybrid modelling (Thompson M. L. and Kramer, M. A., AIChE Journal, 1991, 40, 1328–1340) has become a popular alternative. It has been applied to many different processes and has shown a marked improvement on the now more traditional data-based models such as artificial neural networks (Psichogios D. C. and Ungar L. H., AIChE Journal, 1992, 38, 1499–1511) and NARMAX (Billings S. A., Gray J. O. and Owens D. H., Nonlinear Systems Design. Peter Peregrinus Ltd, 1984) structures. Hybrid models consist of a ‘black box’ model combined with a model of predefined structure (often mechanistic/first principles model) thereby gaining the advantages of both modelling techniques, namely the non-linear capabilities of a generally structured neural network and the extrapolation capabilities of a more rigid model. In this paper the ability of two different hybrid modelling techniques, combining an artificial neural network with a model of appropriate structure, to predict loss of head of pressure profiles in a water treatment plant, are compared. Data were obtained at 15-min intervals from on-line plant sensors. The input variables used were incoming water flowrate, raw water turbidity, supernatant return flowrate, pH of water entering the filters and solids build up on the filters. Results show the serial structure to be more robust allowing extrapolation outside the range of the training data set as compared to the parallel arrangement yielding a 20% reduction in RMS validation error.