An observer for partially obstructed wood particles in industrial drying processes

TL;DR

This paper presents a real-time observer combining reduced order models and extended Kalman filters to estimate water content and temperature of partially obstructed wood particles in industrial drying, enhancing efficiency without expensive sensors.

Contribution

It introduces a novel observer that reliably estimates water content and temperature from surface temperature measurements even with partial surface obstruction in industrial drying processes.

Findings

Observer works reliably with partial surface measurements

Reduced order models enable real-time computation

Method improves drying efficiency without costly sensors

Abstract

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