Modelling and predicting flow regimes using wavelet representations

Goodwin, D.A., Barber, S. & Aykroyd, R.G.

The aim of industrial process control is to convert measurements, taken while the process is evolving, into parameters which can be used to control the process. To be of practical use this must all be computationally e cient allowing real-time feedback. Electrical tomography measurements have the potential to provide useful data without intruding into the industrial process, but produce highly correlated and noisy data, and hence need sensitive analysis. The commonly used approaches, based on regularized image reconstruction are slow, and still require image post-processing to extract control parameters. An alternative approach is to directly work with the measurement data. We demonstrate an approach using wavelets to relate such electrical measurements to the state of flow within a pipe, and hence classify the state of the flow to one of a number of regimes. Wavelets are an ideal tool for our purpose since their multi-scale nature enables the e cient description of both transient and long-term signals. The resulting wavelet models can be used to classify flow into one of a set of regimes, either for later study of the flow profile or for monitoring of an ongoing process. We illustrate our methods by application to simulated data sets.