simplified process model for cfb combustion of different … · 2018-06-27 · berlin, 9th...

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

Simplified process model for CFB combustion of different biomassChair for Energy Process Engineering / Daniel BernhardtBerlin, 9th International Freiberg Conference // 5 June 2018

Slide 1

Motivation – Increasing Fuel Flexibility in biomass energy use

Fuel range

- Small amounts (<10.000 t/a)- Different sources Variable fuel properties

Varying Flue gasemissions

Deposits/Corrosion vs.Availability/Costs

Source: Frandsen, 2011

Challenges

Flexible biomass utilization needs biomass specific process optimization

Power Plants

- Design and operation is optimized on „regular fuel“

Source: BEB Bioenergie Baden


Slide 2

Model-based process control for biomass combustion in CFB boilers

Fuel

(C,H,O,N,S)

?

Fuel,

Reaction gas,

Additives

Control

Variables

Identification of

unknown fuel

propertiesPower Plant (e.g. CFBC)


Process data

acquisition

Monitoring (Sensors + Models)

Process analysis

(Online-Balancing+

Structural analysis)

New targets for control variables

Prediction &

Optimization


Slide 3

Identification of Unknown Fuel Properties Exemplary for 300 kWth CFB pilot plant


Slide 4

Identification of Unknown Fuel Properties Online-Balancing basics

Energy-Balance

Elemental-Mass-Balances

Assumptions: xFuel,N = xFuel,S = 0

xC, xH, xO, xH2O

&hu

Additional Statistic

xC, xH, xO, xH2O


Slide 5

Identification of Unknown Fuel Properties Online-Balancing - Results

hu,fuel,calculated hu,fuel,measured

xC,fuel,calculated ; xC,fuel,measured

xO,fuel,calculated ; xO,BS,measured

xH,fuel,calculated ; xH,fuel,measured

xH2O,fuel,calculated ; xH2O,fuel,measured


Slide 6

Identification of Unknown Fuel Properties Structural Analysis – Composition and Heating Value

i…C, H, O, N, S

j…Structural component


Slide 7

Identification of Unknown Fuel Properties Structural Analysis – Mathematics

Objective function

Constraints

Lo

we

rb

ou

nd

Up

pe

rb

ou

nd

Cel [wt.-% daf]

HC

el

[wt.

-% d

af]

Online-balance

Linear combination


Slide 8

Identification of Unknown Fuel Properties Structural Analysis – Results

Cellulose

Hemicellulose

Lignin

Extractives

Holocellulose


Slide 9

Identification of Unknown Fuel Properties Structural Analysis – Results


Slide 10

Model-based process control for biomass combustion in CFB boilers

Fuel

(C,H,O,N,S)

?

Fuel,

Reaction gas,

Additives

Control

Variables

Identification of

unknown fuel

propertiesPower Plant (e.g. CFBC)


Process data

acquisition

Monitoring (Sensors + Models)

Process analysis

(Online-Balancing+

Structural analysis)

New targets for control variables

Prediction &

Optimization


Slide 11

Simplified CFB combustion model

Simplified Reactor Model300 kWth CFB pilot plant


Slide 12

Simplified CFB combustion modelSelected 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 differencebetween 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 13


Wood pellets Malt res. pellets Cherry pits

Laboratory fluidized bed reactor experiments

Feedstock

• Fuel conversion sub model is basically applicable

• 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.: 23rd International Conference of Fluidized Bed Conversion FBC, Seoul, 2018


Slide 14


Simulation Results – Steady State


Slide 15


Simulation Results – Steady State


Slide 16

Simplified CFB combustion modelSimulation Results – Process dynamics (grain residues)


Slide 17

Summary and Outlook

• Model-based process control concept for emission reduction during biomass combustion was exemplarydeveloped 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, N2O, 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 18

Thank you for your interest!

Contact

Dr.-Ing. Daniel Berhardtwissenschaftlicher MitarbeiterTechnische Universität DresdenFakultät MaschinenwesenInstitut für Verfahrenstechnik und UmwelttechnikProfessur für Energieverfahrenstechnik01062 Dresden

Tel.: +49 (351) 463 33 143Fax: +49 (351) 463 37 753E-Mail: [email protected] web: http://energieverfahrenstechnik.deBüro: Walther-Pauer-Bau, Zimmer 208

simplified process model for cfb combustion of different … · 2018-06-27 · berlin, 9th...

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