Original scientific paper
https://doi.org/10.13044/j.sdewes.d7.0263
Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks
Robert Mikulandrić
; Department of Energy, Power Engineering and Ecology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10002 Zagreb, Croatia
Dorith Böhning
; Institute of Power Engineering, Faculty of Mechanical Science and Engineering, Technical University Dresden, George-Bähr-Strasse 3b, 01069 Dresden, Germany
Dražen Lončar
; Department of Energy, Power Engineering and Ecology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10002 Zagreb, Croatia
Abstract
To improve biomass gasification efficiency through process control, a lot of attention had been given to development of models that can predict process parameters in real time and changing operating conditions. The paper analyses the potential of a nonlinear autoregressive exogenous model to predict syngas temperature and composition during plant operation with variable operating conditions. The model has been designed and trained based on measurement data containing fuel and air flow rates, from a 75 kWth fixed bed gasification plant at Technical University Dresden. Process performance changes were observed between two sets of measurements conducted in 2006 and 2013. The effect of process performance changes on the syngas temperature was predicted with prediction error under 10% without changing the model structure. It was concluded that the model could be used for short term predictions (up to 5 minutes) of syngas temperature and composition as it strongly depends on current process measurements for future predictions. For long term predictions other types of dynamic neural networks are more applicable.
Keywords
Biomass gasification; Fixed bed reactor; Gasification modelling; Neural networks; Nonlinear autoregressive network with exogenous models.
Hrčak ID:
226027
URI
Publication date:
31.3.2020.
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