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L. Böhler, M. Kozek et al.:
"Key Influence Factors in Modelling of Biomass Combustion in Small and Medium Scale Furnaces";
Talk: e-nova 2016 Internationaler Kongress: Nachhaltige Technologien Gebäude - Energie - Umwelt, Pinkafeld; 2016-11-24 - 2016-11-25; in: "enova 2016", L. Böhler (ed.); Leykam, Nachhaltige Technologien (2016), ISBN: 978-3-7011-0372-0.

The combustion of biomass provides a potential economic and environmentally friendly source of renewable energy. In order to obtain the full potential, both the supply of biomass and the combustion process have to be as environmentally sustainable and efficient as possible. Grate firing technology is already considered to have reached relatively high standards, whereas the contribution of control strategies to optimal operation conditions is not yet fully explored. In this work the interaction of key influencing factors for modelling of small to medium scaled grate furnaces is investigated to derive information about the most significant modelling parameters.
Complex nonlinear thermo-chemical processes occur inside the furnace during the combustion of biomass. The industrial applicability of advanced control strategies, like Model Predictive Control (MPC), can be negatively influenced by complex modelling approaches due to higher computational effort. Therefore, the significant key parameters to describe the essential combustion dynamics inside the furnace need to be obtained without the loss of quantitative process information. This knowledge can be used for the design of robust, easily tunable models for MPC. The results presented contribute to optimal performance modeling by pointing out the most significant parameters, which show the highest impact for changes in the combustion modeling process. The obtained information can be used for the future design of model based advanced control strategies and for different modeling approaches as well. By comparison of the different models, furnace scales and operating points, feedback about specific parameter settings and their usability for model tuning and adjustment is presented.

Parameter Sensitivity, Biomass, Combustion