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In order for biomass drying processes to be efficient, it is crucial toachieve the target residual water content within a close margin, since moreconservative drying would result in a waste of energy. A method for a reliableestimation of the water content is therefore of obvious importance. Ideally,such a method does not require any expensive sensors. We show reduced ordermodels and extended Kalman filters can be combined to reliably determine thewater content and temperature of wood particles based on only surfacetemperature measurements. The proposed observer works reliably if measurementsare only available for parts of a particle face. It can therefore still beapplied if particle surfaces are partially obstructed, which is a prerequisitefor use in industrial processes and units, such as rotary dryers. The extendedKalman filter uses a reduced order model that is obtained by applying properorthogonal decomposition and Galerkin projection to coupled PDEs that modelheat conduction and water diffusion in anisotropic particles. In contrast tothe original PDE simulation model, the reduced model and the filter based on itare suitable for real time computations and monitoring.

An observer for partially obstructed wood particles in industrial drying processes