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|Title:||Automated advanced calibration and optimization of thermochemical models applied to biomass gasification and pyrolysis||Authors:||Kraft, Markus
Paul, Manosh C.
Brownbridge, George P. E.
Salem, Ahmed M.
Bhave, Amit N.
|Issue Date:||2018||Source:||Bianco, N., Paul, M. C., Brownbridge, G. P. E., Nurkowski, D., Salem, A. M., Kumar, U., … Kraft, M. (2018). Automated advanced calibration and optimization of thermochemical models applied to biomass gasification and pyrolysis. Energy & Fuels, 32(10), 10144-10153. doi:10.1021/acs.energyfuels.8b01007||Series/Report no.:||Energy & Fuels||Abstract:||This paper presents a methodology that combines physicochemical modeling with advanced statistical analysis algorithms as an efficient workflow, which is then applied to the optimization and design of biomass pyrolysis and gasification processes. The goal was to develop an automated flexible approach for the analyses and optimization of such processes. The approach presented here can also be directly applied to other biomass conversion processes and, in general, to all those processes for which a parametrized model is available. A flexible physicochemical model of the process is initially formulated. Within this model, a hierarchy of sensitive model parameters and input variables (process conditions) is identified, which are then automatically adjusted to calibrate the model and to optimize the process. Through the numerical solution of the underlying mathematical model of the process, we can understand how species concentrations and the thermodynamic conditions within the reactor evolve for the two processes studied. The flexibility offered by the ability to control any model parameter is critical in enabling optimization of both efficiency of the process as well as its emissions. It allows users to design and operate feedstock-flexible pyrolysis and gasification processes, accurately control product characteristics, and minimize the formation of unwanted byproducts (e.g., tar in biomass gasification processes) by exploiting various productivity-enhancing simulation techniques, such as parameter estimation, computational surrogate (reduced order model) generation, uncertainty propagation, and multi-response optimization.||URI:||https://hdl.handle.net/10356/80805
|ISSN:||0887-0624||DOI:||10.1021/acs.energyfuels.8b01007||Rights:||© 2018 American Chemical Society (ACS). All rights reserved. This paper was published in Energy & Fuels and is made available with permission of American Chemical Society (ACS).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCBE Journal Articles|
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