Simplified process model for CFB combustion of different biomass as part of an assistance system for emission reduction

Transcription

1 Simplified process model for CFB combustion of different biomass as part of an assistance system for emission reduction

2 Motivation Increasing Fuel Flexibility in biomass energy use Fuel range Power Plants Challenges Varying Flue gas emissions - Small amounts (< t/a) - Different sources Variable fuel properties Source: BEB Bioenergie Baden - Design and operation is optimized on regular fuel Flexible biomass utilization needs biomass specific process optimization Deposits/Corrosion vs. Availability/Costs Source: Frandsen, 2011 Slide 1

3 Model-based process control for biomass combustion in CFB boilers Identification of unknown fuel properties Power Plant (e.g. CFBC) Process data acquisition Monitoring (Sensors + Models) Process analysis (Online-Balancing+ Structural analysis) Fuel (C,H,O,N,S)? Control Variables Fuel, Reaction gas, Additives Source: BEB Bioenergie Baden Prediction & Optimization New targets for control variables Slide 2

4 Identification of Unknown Fuel Properties Exemplary for 300 kw th CFB pilot plant Slide 3

5 Identification of Unknown Fuel Properties Online-Balancing basics Elemental-Mass-Balances Assumptions: x Fuel,N = x Fuel,S = 0 Additional Statistic Energy-Balance x C, x H, x O, x H2O & h u Slide 4

6 Identification of Unknown Fuel Properties Online-Balancing - Results h u,fuel,calculated h u,fuel,measured x C,fuel,calculated ; x C,fuel,measured x O,fuel,calculated ; x O,BS,measured x H,fuel,calculated ; x H,fuel,measured x H2O,fuel,calculated ; x H2O,fuel,measured Slide 5

7 Identification of Unknown Fuel Properties Structural Analysis Composition and Heating Value i C, H, O, N, S j Structural component Slide 6

8 HCel [wt.-% daf] Lower bound Upper bound Identification of Unknown Fuel Properties Structural Analysis Mathematics Linear combination Objective function Constraints Online-balance Cel [wt.-% daf] Slide 7

9 Identification of Unknown Fuel Properties Structural Analysis Results Extractives Lignin Hemicellulose Holocellulose Cellulose Slide 8

10 Identification of Unknown Fuel Properties Structural Analysis Results Slide 9

11 Model-based process control for biomass combustion in CFB boilers Identification of unknown fuel properties Power Plant (e.g. CFBC) Process data acquisition Monitoring (Sensors + Models) Process analysis (Online-Balancing+ Structural analysis) Fuel (C,H,O,N,S)? Control Variables Fuel, Reaction gas, Additives Source: BEB Bioenergie Baden Prediction & Optimization New targets for control variables Slide 10

12 Simplified CFB combustion model 300 kw th CFB pilot plant Simplified Reactor Model Slide 11

13 Simplified CFB combustion model Selected model specifications Fluid dynamics in the riser (combustion chamber): approach of Kunii and Levenspiel Fuel conversion: Temperature and concentration gradients inside particle are neglected Drying: Physical approach based on mass transfer caused by concentration difference between particle and surrounding gas Devolatilization: multi-component one stage mechanism component volatile + char with kinetic data from Hajaligol (cellulose), Gosh (hemicellulose) and Nunn (lignin); Extractives were approximated by data of hemicellulose Heterogeneous char oxidation: approach of Field Slide 12

14 Simplified CFB combustion model Laboratory fluidized bed reactor experiments Feedstock Fuel conversion sub model is basically applicable Wood pellets Malt res. pellets Cherry pits Differences in post-devolatilization stage are probably caused by the neglect of temperature and concentration gradients inside the particle and inaccuracies in the kinetic data Bernhardt, D.; Beckmann, M.: 23 rd International Conference of Fluidized Bed Conversion FBC, Seoul, 2018 Slide 13

15 Simplified CFB combustion model Simulation Results Steady State Slide 14

16 Simplified CFB combustion model Simulation Results Steady State Slide 15

17 Simplified CFB combustion model Simulation Results Process dynamics (grain residues) Slide 16

18 Summary and Outlook Model-based process control concept for emission reduction during biomass combustion was exemplary developed for a CFBC pilot plant. Identification of unknown fuel properties Prediction of fuel burn out and pollutant emissions The core idea of the concept is based on the fact that the biomass composition and the fuel conversion is attributed to the structural composition. Despite strong model simplifications a good agreement between measurement and calculation results is achieved in many points. Important model refinements include the Extension of the data base concerning the extractives (composition, kinetic data) Predictive process model (desulfurization, N 2 O, PSD) With respect to the transfer to real biomass plants the steam cycle and the process optimization still needs to be supplemented. The final goal is to realize the model concept as an assistance system for biomass power plants, which provides the operator with recommendations for the flexible use of different biomass fuels. Slide 17

19 Thank you for your interest! Contact Dr.-Ing. Daniel Berhardt wissenschaftlicher Mitarbeiter Technische Universität Dresden Fakultät Maschinenwesen Institut für Verfahrenstechnik und Umwelttechnik Professur für Energieverfahrenstechnik Dresden Tel.: +49 (351) Fax: +49 (351) web: Büro: Walther-Pauer-Bau, Zimmer 208 Slide 18