2019/11/12 by Marc Oliver Berner, Berner, Marc Oliver, Viktor Scherer +3 · 1 citation
Decision Sciences · #FOS: Mathematics #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.1911.04881
openalex publication_date 2019/11/12 · openalex created_date 2023/02/18 · openalex updated_date 2026/07/28
In order for biomass drying processes to be efficient, it is crucial to\nachieve the target residual water content within a close margin, since more\nconservative drying would result in a waste of energy. A method for a reliable\nestimation of the water content is therefore of obvious importance. Ideally,\nsuch a method does not require any expensive sensors. We show reduced order\nmodels and extended Kalman filters can be combined to reliably determine the\nwater content and temperature of wood particles based on only surface\ntemperature measurements. The proposed observer works reliably if measurements\nare only available for parts of a particle face. It can therefore still be\napplied if particle surfaces are partially obstructed, which is a prerequisite\nfor use in industrial processes and units, such as rotary dryers. The extended\nKalman filter uses a reduced order model that is obtained by applying proper\northogonal decomposition and Galerkin projection to coupled PDEs that model\nheat conduction and water diffusion in anisotropic particles. In contrast to\nthe original PDE simulation model, the reduced model and the filter based on it\nare suitable for real time computations and monitoring.\n