Symbol-sequence Statistics for Monitoring Fluidization

We propose that data-symbolization methods derived from nonlinear dynamics and chaos theory can be useful for characterizing and monitoring patterns in fluidized-bed measurement signals. Data symbolization involves the discretization of a measurement signal into a limited set of values. In this discretized form, the measurements can be processed very efficiently to detect dynamic patterns that signify various types of physical phenomena, including bubbling, slugging, and transitions between fluidization states. Besides computational efficiency, symbolic methods are also robust when noise is present. Using various types of measurements from experimental beds, we illustrate specific examples of how symbolization can be applied to fluidization diagnostics. We also suggest directions for future research.