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


Mikulandrić, Robert; Böhning, Dorith; Lončar, Dražen
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, 1 (2019), 1-17 doi:10.13044/j.sdewes.d7.0263 (međunarodna recenzija, članak, znanstveni)


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

Autori
Mikulandrić, Robert ; Böhning, Dorith ; Lončar, Dražen

Izvornik
Journal of sustainable development of energy, water and environment systems (1848-9257) 1 (2019); 1-17

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Biomass gasification, Fixed bed reactor, Gasification modelling, Neural networks, Nonlinear autoregressive network with exogenous models.

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Citiraj ovu publikaciju

Mikulandrić, Robert; Böhning, Dorith; Lončar, Dražen
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, 1 (2019), 1-17 doi:10.13044/j.sdewes.d7.0263 (međunarodna recenzija, članak, znanstveni)
Mikulandrić, R., Böhning, D. & Lončar, D. (2019) 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, 1, 1-17 doi:10.13044/j.sdewes.d7.0263.
@article{article, year = {2019}, pages = {1-17}, DOI = {10.13044/j.sdewes.d7.0263}, keywords = {Biomass gasification, Fixed bed reactor, Gasification modelling, Neural networks, Nonlinear autoregressive network with exogenous models.}, journal = {Journal of sustainable development of energy, water and environment systems}, doi = {10.13044/j.sdewes.d7.0263}, volume = {1}, issn = {1848-9257}, title = {Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks}, keyword = {Biomass gasification, Fixed bed reactor, Gasification modelling, Neural networks, Nonlinear autoregressive network with exogenous models.} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


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