automatic cfd optimisation of biomass combustion...

Automatic CFD optimisation of biomass combustion plants

Ali Shiehnejadhesar

IEA Bioenergy Task 32 workshop Thursday 6th June 2013

Contents

■ Scope of work ■ Methodology

■ CFD model for biomass grate furnaces ■ Optimisation routine

■ Case studies performed ■ Results ■ Summary and conclusions

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Scope of work

■ Development of a tool for the automatic performance of CFD-based parameter studies for the optimisation of biomass grate furnaces

■ Link of automatic parameterisation and optimisation routines with an in-house developed CFD model for biomass grate furnaces

■ Test of the model for a 180 kW grate furnace of BE2020+ ■ Reduction of carbon monoxide emissions and the energy demand of the

secondary air fan (pressure loss) by changing the diameter and angle of the secondary air nozzles

■ Performance of a manual parameter study ■ Performance of an automatic parameter study and benchmarking against the

manual study ■ Investigation of the grid type influence on the overall simulation time and

accuracy

■ Application of the method developed for a 15 KW fixed bed pellet furnace

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Methodology CFD model

■ In-house developed empirical packed bed combustion model for biomass grate furnaces

■ Simulation of gas phase combustion (CFD models of Ansys/Fluent) ■ Realizable k-ε Model: turbulence ■ Discrete Ordinate Model: radiation ■ Eddy Dissipation Model with global 3 step methane mechanism:

interaction of turbulence and chemistry

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Methodology Optimisation routine I

■ Weight function ■ Development of a weight function to combine the two optimisation

parameters (CO emissions and pressure loss over the secondary air nozzles) in a common function

■ A linear correlation to the weight function has been assumed regarding the

pressure loss (proportional to the energy demand of the fan) ■ A polynomial function with a strong increase at a chosen emission limit was

assumed for the CO emissions

1

CO

W A B PY

α

= ⋅ + ⋅ ∆

Explanations: W…weight function; YCO…CO mole fraction in ppmv; α…constant factor; B…constant factor; ∆P… pressure loss in Pa

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Methodology Optimisation routine II

■ Optimisation routine Parameterisation of the geometry and definition of design

points based on selected design variables Automatic performance of CFD simulations for the defined

design points with the ANSYS Workbench Evaluation of the output parameters and calculation of the

weight function for the design points Minimisation of the weight function to find the optimum

geometric configuration

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Furnace investigated CFD model for the pilot-scale grate furnace of BE2020+

Boiler load = 180 kWth

Adiabatic flame temperature = 937 °C O2 content flue gas = 8.4 Vol% dry Total air ratio = 1.67 Primary air ratio = 0.83 Flue gas recirculation ratio = 0.3

Secondary air nozzles

Furnace outlet (boiler inlet)

Surface of fuel bed

Secondarycombustionchamber

Symmetryplane

Primary combustion chamber

Flue gas re-circulation nozzles

Angle of the nozzles to the horizontal plane

Flue gas path

Fuel Miscanthus

Unit Value C wt.% d.b. 48.3 H wt.% d.b. 5.9 O wt.% d.b. 43.0 N wt.% d.b. 0.34 Moisture content wt.% w.b. 15.4 Ash wt.% d.b. 2.2 GCV MJ/Kg d.b. 19.31 NCV MJ/Kg w.b. 14.86

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Results Automatic versus manual optimisation

Calculated CO emissions and pressure loss (left) as well as weight function (right) for the automatic and manual optimisation method

Lines – Manual optimisationCircles – Automatic optimisation

Diameter of secondary air nozzles (mm)

Manual optimisation (700.000 tetrahedral cells)Automatic optimisation ( 1 million cells)

Gdmin = 15.15 (mm)

Kdmin = 17 (mm)


K Global minimum (manual optimisation method) G Global minimum (automatic optimisation method)

Manual optimisation (700.000 tetrahedral cells) Automatic optimisation (1 million tetrahedral cells)

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Results Grid dependency

Effect of mesh type on the calculated CO emissions and pressure loss (left) and weight function for different mesh types (right)

Circles – Tetrahedral cellsCrosses – Polyhedral cells


Tetrahedral cells ( 1 million cells)Polyhedral cells (250.000 cells)

Gdmin = 15.15 (mm)

Hdmin = 15.78 (mm)


G Global minimum (tetrahedral cells) H Global minimum (polyhedral cells)

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Results 3D Weight function

Weight surface from automatic optimisation and tetrahedral grid type calculated with 80 design points

Explanations: tetrahedral mesh type with 1 million cells

Cdmin = 15.78 (mm) αmin = 5.08 (deg)

Example of local minimumGlobal minimum

Nozzle angle (deg) Nozzle diameter (mm)

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Summary and conclusions – model test pilot-scale grate furnace

■ Development and test of a new CFD-based automatic optimisation routine for a pilot-scale biomass grate furnace concerning the minimisation of CO emissions and pressure loss

■ Considerably higher impact of the nozzle diameter on CO emissions and pressure loss than the nozzle angle

■ Good agreement between results generated by the automatic optimisation routines in comparison to the manual optimisation method

■ Significant reduction of simulation time compared to the manual method by a factor of 8 for the tetrahedral mesh type

■ With the polyhedral mesh type a further reduction of the calculation time could be achieved (factor of 32 compared to the manual optimisation) even though there were slight deviations from the tetrahedral grid concerning CO emissions

■ A further improvement of the mesh concerning accuracy without a significant increase of computation time can be achieved by a local mesh refinement in the nozzle exit region

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Case study II CFD model of a 15 KW pellet boiler

Grate

Secondary air

Fuel feeding

Flue gas outlet

Fuel bed

Boiler (blue colors)

Secondary air nozzles

α

Nominal boiler capacity : 15 kW

Adiabatic flame temperature = 1.502 °C Primary air ratio = 0.41 Total air ratio = 1.53 Number of secondary air nozzles = 11 Fuel : Soft wood pellets (moisture content

(wt.% w.b.) : 6.54)

Pellet furnace

Burner

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a

Combustion chamber

b

■ Design parameters: ■ Diameter of the secondary air nozzles (6 – 10 mm) ■ Angle of the secondary air nozzles to burner axis (10° – 30°)

■ Target values investigated: ■ CO concentration at the entrance to the heat exchanger ■ Pressure drop over the secondary air supply and nozzles ■ Minimisation of the weight function with respect to pressure

drop and CO concentration ■ Constraints:

■ Temperature of burner (a) < 700 °C ■ Pressure drop < maximum under pressure in the furnace

achievable by the flue gas fan (b) = 150 Pa

Parameters investigated and target values

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Results CO concentration and pressure drop

■ The pressure drop increases with increasing the angle of the secondary air nozzles ■ The pressure drop increases with decreasing the diameter of the secondary air nozzles ■ The CO emissions decrease with decreasing the diameter of the secondary air nozzles ■ No clear trend concerning CO burnout as a function of angle was found due to flame

instabilities observed

CO concentration [ppmv] at the entrance to the heat exchanger (left) and pressure drop over the secondary air supply and nozzles [Pa] (right)


CO

em

issi

on (p

pmV

)

Pressure constraint (150 Pa)


Pre

ssur

e lo

ss (P

asca

l)

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Results Feasible design concerning nozzle angle and diameter

Maximum temperature of the burner [°C] in dependence of the angle and diameter of the secondary air nozzles to the burner axis

Feasible design area

∆P < 150 Pa

Nozzle diameter (mm)

Bur

ner w

all t

empe

ratu

re (°

C)

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Maximum burner wall temperature [°C] in dependence of the angle of the secondary air nozzles at a nozzle diameter of 7.5 [mm]

α = 10° α = 20° α = 30°

Results Maximum temperature of the burner wall

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Results Weight function

Optimisation function (left) and Pareto front (right) Explanations: black points: not-optimal solutions, red points: solutions on the pareto front

■ The optimum nozzle configuration was found at an angle of 10° and a diameter of 7.5 mm.

■ The pareto set (red points) shows the variants with the lowest CO emissions at a given pressure drop or with the lowest pressure drop for a given CO emission limit

d = 7.5 α = 10° Nozzle angle (deg)

Nozzle diameter (mm)

Wei

ght (

-)

1: Optimum solution of weight function (7.5 mm / 10°) 2: 8 mm / 10° 3: 7.5 / 15° 4: Original configuration (7.2 mm / 20°) : Pareto front

1

2

3 4

Pressure constraint (150 Pa)

Pressure loss (Pa)

CO

con

cent

ratio

n (p

pmV)

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Summary and conclusions – case study 15 kW pellet furnace

■ A high exit velocity of the secondary air jets improves the CO burnout. ■ An additional increase of the nozzle angle shortens the flame length and

further reduces the CO emissions (swirling flow). ■ However, the burner wall temperature increases with a shorter flame length. ■ Moreover, the pressure drop increases with larger the nozzle angles. ■ The pareto front represents the design variables which have the lowest CO

emission for a chosen pressure loss and the lowest pressure loss for a chosen CO emission limit, respectively.

■ Hence, the pareto front provides valuable information about the best design variant regarding a certain parameter (e.g. CO emission limits) for defined side constraints (e.g. fan pressure increase).

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■ The new automatic CFD optimisation routine works well and shows a large potential concerning:

■ targeted geometry optimisation ■ considerable reduction of manpower needed

■ Future work will focus on:

■ implementation of a direct optimisation routine in order to reduce the number of simulated cases and the simulation time to find the optimum

■ extension of the number of design parameters (from 2 to 4) ■ implementation of an interface to the empirical packed bed model in order to

investigate variations of operating conditions like air staging and fuel heat load ■ Test of the routines for real-scale plants focusing on:

■ automatic geometry optimisation ■ automatic optimisation of plant operation (e.g. variation of air staging and

simulation of CO-λ-characteristics as a basis for an optimised plant control) ■ automatic scaling of plants

Overall conclusions and outlook

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Thank you for your attention

Ali Shiehnejadhesar BIOENERGY 2020+ GmbH

Inffeldgasse 21b, 8010 Graz, Austria Tel.: +43 (0)316 8739230

E-mail: [email protected]

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automatic cfd optimisation of biomass combustion...

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