Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks

Journal of Sustainable Development of Energy, Water and Environment Systems. 2020;8(1):145-161 DOI 10.13044/j.sdewes.d7.0263

 

Journal Homepage

Journal Title: Journal of Sustainable Development of Energy, Water and Environment Systems

ISSN: 1848-9257 (Print)

Publisher: SDEWES Centre

LCC Subject Category: Technology | Social Sciences: Industries. Land use. Labor: Economic growth, development, planning

Country of publisher: Croatia

Language of fulltext: English

Full-text formats available: PDF

 

AUTHORS

Robert Mikulandric ( 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 )

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

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.