Modelling of Material and Energy Balance of Biogas ... · PDF fileModelling of Material and Energy Balance of Biogas Production Process Mandy Gerber 2nd GERG Academic Network Event

Modelling of Material and Energy Balance of Biogas Production Process

Mandy Gerber

2nd GERG Academic Network Event

Brussels, June 2010

2Mandy Gerber | 2nd GERG Academic Network Event | June 2010

Basics

Biogas • Metabolic waste product

Production• Degradation of organic matter

(protein, carbohydrate, fats)in anaerobic environment

• Natural process identical toswamp / marsh, cattle rumen,sewage sludge, dumps, rice field

Input material• Manure, energy crops, organic waste

10.01 – 5N2

0.030.01 – 2O2

<10 – 1H2

0.7*0.01 – 2.5*NH3

500*10 – 30,000*H2S

ProcessRangeVol-%

AverageVol-%

CH4 45 – 70 60

CO2 25 – 55 35

H2Od(25°C, 1 atm)

0 – 10 3.1

* in mg/m³ DVGW, 2005


Basics

IEKP of German government• Integrated energy and climate programme (measures to

increase energy efficiency and use of renewable energies)• Objective: replace 10% of gas supply by gas out of biomass

(60·109 kWh biogas per year until 2020, 100·109 kWh biogas per year until 2030)

Quality of biogas for feeding in gas distribution net• Raw biogas: HHV = 5.2 - 8.2 kWh/m³, WS = 18.0 - 33.1 MJ/m³

(average: HHV = 6.9 kWh/m³, WS = 26.1 MJ/m³)

• Upgraded biogas: HHV = 10.8 kWh/m³, WS = 51.3 MJ/m³• Depending on net and biogas quality as exchange gas or

additional gas(L-Gas: WS = 37.8 - 46.8 MJ/m³, H-Gas: WS = 46.1 - 56.5 MJ/m³,HHV = 8.4 - 13.1 kWh/m³)


Basics

Status biogas plants in Germany• 30 of about 4500 biogas plants with feed in a gas distribution net

(about 2.6% of government goal)

• To reach the goal of government: Construction of 100 – 120 biogas plants per year until 2020 will be needed(700 Nm³/h, 8000 operating hours)

8501043

13601608 1760

2010

2690

3711

3280

4780

4000

1600

1400

1270

950

665

2471901601117849

0

1000

2000

3000

4000

5000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009*

num

ber o

f bio

gas

plan

ts

0

200

400

600

800

1000

1200

1400

1600

pow

er in

stal

led

in M

W

biogas plants

installed power

EEG1. modified

EEG2. modiefied

EEG


Basics

Typical biogas plant• Two-stages• Feed: energy crops and manure


Modelling

Aspen Custom Modeller• Modelling

of importantplantcomponents


Material Balance Fermenter

Process parameter • Quality of input material

(organic components, inhibitors, trace elements)

• Quantity of inputmaterial (organic loading rate, dilution)

• pH value / alkalinity / phase equilibrium

• Temperature• Adaption of

microorganism

0.0

0.1

0.2

0.3

0.4

0 20 40 60 80 100 120

substrate concentration in mmol/L

spec

ific

grow

th ra

te in

d -1

pH 6.0 pH 6.5 pH 7.0

µmax = 0,4 d-1

KS = 0,033 mmol/LKI = 0,667 mmol/L

biodegradable organic

Hydrolysis(hydrolytic enzymes)

amino acids, mono saccharide fatty acids

Acidogenesis(acidogentic microorganisms)

acetate

CH4, CO2

Methanogenesis(methanogenic microorganisms)

Acetogenesis(acetogenic microorganisms)

21%

100%

proteinscarbo-hydrate fats

complex organic

21%40% 5%

46%

H2, CO2

propionate, butyrate, etc

20% 34%

35% 12% 23% 11% 8% 11%

70% 30%

biodegradable organic

Hydrolysis(hydrolytic enzymes)

amino acids, mono saccharide fatty acids

Acidogenesis(acidogentic microorganisms)

acetate

CH4, CO2

Methanogenesis(methanogenic microorganisms)

Acetogenesis(acetogenic microorganisms)

21%

100%

proteinscarbo-hydrate fats

complex organic

21%40% 5%

46%

H2, CO2

propionate, butyrate, etc

20% 34%

35% 12% 23% 11% 8% 11%

70% 30%



Static models • Baserga

based on gas production → except KTBL and batch testof organic components mostly used for facility design

• Keymer & Schilcherbased on Baserga incl. → derived from evaluating animal feeddegradation coefficients

• Boylebased on elementary → maximum of gas production as referencecomposition state ⋅⎛ ⎞+ − − + +⎜ ⎟

⎝ ⎠⋅⎛ ⎞→ + − − −⎜ ⎟

⎝ ⎠⋅⎛ ⎞+ − + + + + +⎜ ⎟

⎝ ⎠

2

4

2 3 2

34 2 4 2

32 8 4 8 4

32 8 4 8 4

a b c d eb c d eC H O N S a H O

a b c d e CH

a b c d e CO d NH e H S

= ⋅ ⋅∑KoS i i ii

q x VQ q

= ⋅ + ⋅ + ⋅KoS CH CH Lip Lip Prot Protq x q x q x q


0

20

40

60

80

100

0 10 20 30 40 50 60test duration in d

stan

dard

ised

gas

pro

duct

ion

in %

CCM 2

CCM 3


Comparison to literature and laboratory

literaturelabliteraturelabliteraturelabliteraturelab

-1.4-1.4

-186.7

-3.1-43.7-75.4

Corn silage

12.6-21.8-48.7

-33.7-56.4-80.5

CCM

---

10.1-45.0-73.1

GPSCattle manure

Boyle -122.7 -82.1Baserga 21.2 -52.5K&S 21.2 5.4

Deviation in % [100 · (lN,exp/lit – lN,calc) / lN,exp/lit] of specific gas production (lN/kgOM):

inhibition



Comparison to a largescale biogas plant

-2.2-2.51.349.450.550.648.7Vol.-%CH4

2.32.64.147.346.246.145.3Vol.-%CO2

---44.20.1--0.1Vol.-%H2S------2.5Vol.-%NH3

Gas productionK&SBasergaBoylePraxisK&SBasergaBoyle

3.3

154965

3.3

123771

3.3

89554

3.3

88548

0.0

-76.0-76.0

0.0

-40.7-40.7in lN/kgOM -1.1

in lN/kgOM -1.1

H2O Vol.-% 0.0



Henry coefficient, acidcoefficient, vapour pressure

growth rate, vapourpressure

vapour pressureTemperature

CH4, CO2, H2CO2, NH3CO2Gas solubilityVOA, CO2, H2O, NH3CO2, acetic acid, H2OCO2, acetic acid, H2OIonic equilibrium

CH, XP, XL, MS, AA, AC, PR, BU, VA, LCFA, H2, CH4, 7 MO, Comp, IN, IC , PI, SI

organic matter, acetic acid

acetic acidInput material

CH4, CO2, H2, H2OCH4, CO2, NH3, H2OCH4, CO2, H2OGas

highmediumlowComplexitymany input parameters, many dependencies

less input, many outputparameters

process stability, lessinput parameters

Adapted to

1921Degradation stepsADM1Hill & BarthAndrews & Graef

substrate Substrate substrate, NH3, H2, pHInhibition

CH: carbohydrate, XP: protein, XL: lipid, MS: monosaccharide, AA: Amino Acid, LCFA: Long Chain Fatty Acid, VA: Valeric Acid, BU: Butyric Acid, PR: Propionic Acid, AC: Acetic Acid, MO: Microorganism, Comp: Composites, PI: Particulate Inert, SI: Soluble Inert, IN: Inorganic Nitrogen, IC: Inorganic Carbon, A-: Anions, C+: Cations

Dynamic models



Model of Andrews & Graef

Gas phase

biogas = +4 2BG CH COV V V

CO2 production = − ⋅ ⋅2CO m L GV v V T

CH4 production μ= ⋅ ⋅ ⋅ ⋅4 4 / 1CH m L CH XV v V Y X

CO2 partial pressure = − ⋅ ⋅ ⋅ − ⋅2 2CO COLT m G BG

G G

dp pVp v T Vdt V V

Liquid phase

CO2 balance ( )= ⋅ − + + +, 2 ,1 0, 2 ,0 , 2 ,1

D

D D

m COm CO m CO G B C

L

dc V c c T R Rdt V

netto cation balance ( )= ⋅ −, ,1 0, ,0 , ,1

m Zm Z m Z

L

dc V c cdt V

chemical production rate ( )= ⋅ − + −, ,1 , ,10, 3,0 , 3,1

m AC m ZC m HCO m HCO

L

dc dcVR c cV dt dt

gas transfer rate ( )= ⋅ −, 2 , 2 , 2 ,1S DG LA CO m CO m COT K c c

dissolved CO2, saturated

= ⋅, 2 , 2 2Sm CO H CO COc K p

carbonate −= −, 3,1 , ,1 , ,1m HCO m Z m ACc c c

unionised acetic acid + −⋅

= , ,1 , ,1, ,1

m H m ACm HAC

AC

c cc

K

hydrogen +

⋅= , 2 ,1

, ,1, 3,1

DC m COm H

m HCO

K cc

c

pH value ( )+= − , ,1log m HpH c

Biological phase

organism balance ( ) μ= ⋅ − + ⋅010 1 1

L

VdX X X Xdt V

acetic acid balance ( ) μ= ⋅ − − ⋅, ,1 0

, ,0 , ,1 1/

m ACm AC m AC

L X S

dc V c c Xdt V Y

growth rate μμ =

+ +

max

, ,1

, ,1

1 m HAcS

m HAc I

cKc K

biological production rate CO2

μ= ⋅ ⋅2 / 1B CO XR Y X

Coefficients

,a ACK 10-4,5 ionisation constant acetic acid at 38°C

,a CK 10-6 ionisation constant CO2 at 38°C

, 2H COK 0,024272 kmol/bar·m³ Henry’s law constant CO2 at 38°C

, 2LA COK 100 d-1 gas transfer rate

IK 0,667 mol/m³ inhibition constant

SK 0,0333 mol/m³ saturation constant

μmax 0,04 d-1 maximum growth rate

2 /CO XY 47 kmolCO2/kmolX Yield coefficient biomass to CO2

4 /CH XY 47 kmolCH4/kmolX Yield coefficient biomass to CH4

/X ACY 0,02 kmolX/kmolAC Yield coefficient acetic acid to biomass



Spontanously triplication of feeding

02468

1012141618

0 5 10 15 20test duration in d

gas

prod

uctio

n in

l/da

y

1 to 3 l/day

1 to 2 l/day

0

10

20

30

40

50

60

0 5 10 15 20

test duration in d

met

hane

in v

ol.-%

1 to 3 l/day

1 to 2 l/day

0.000.020.040.060.080.100.120.140.160.18


subs

trate

con

cent

ratio

n in

km

ol/m

³

1 to 3 l/day

1 to 2 l/day

0.00

0.05

0.10

0.15

0.20

0.25

0.30


grow

th ra

te in

1/d

ay

1 to 3 l/day

1 to 2 l/day

0.00000.00050.00100.00150.00200.00250.00300.00350.00400.0045

0 5 10 15 20

test duration in d

mic

roor

gani

sm c

once

ntra

tion

in k

mol

/m³

1 to 3 l/day

1 to 2 l/day

0.01.02.03.04.05.06.07.08.09.0


pH

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

conc

entra

tion

HC

O3

in k

mol

/m³

1 to 3

1 to 2


Summary

Conclusion• Tool to design and optimise biogas plants• Investigation of process failures and reaction rates possible

Outlook• More data needed for validation, especially for dynamic models• expand simulation for modeling of gas treatment


Thank you for your attention!


Energy Balance Fermenter

Power demand mixer • Danish fermenter: one central, slow ongoing agitator• Agricultural fermenter: one or more submersible, fast ongoing

motor-driven agitators• Calculation by Newton-Number (geometry, agitator speed, flow,

density / viscosity - composition)

Danishfermenter

Agriculturalfermenter

Number of mixer 1 3Power 18.9 kW 38.3 kW

Energy Demand454 kWh/d

(24 h/d)77 kWh/d

(2 h/d)Feed: manure and corn silage (1:1), 3.65 kgOM/(m³d), volume 3000 m³, retention time 74d, temperature 38°C

ρ= ⋅ ⋅ ⋅3 5Mix RP Ne n d

Danish Fermenter

Agricultural Fermenter


Energy Balance Fermenter

Heat demand • Heat loss via roof, wall and ground (heat transfer coefficient of

every layer depending on temperature, wind speed, geometry, fluid properties)

• Dissipation Mixer• Enthalpy balance für heat of reaction and temperature difference

substrate / biogas (heating value, evaporation, heat capacity)

Danishfermenter

Agriculturalfermenter

Heat Demand 90.3 kW 38.3 kW2167 kWh/d 2936 kWh/d

Feed: manure and corn silage (1:1), 3.65 kgOM/(m³d), volume 3.000 m³, retention time 74d, temperaturefermenter 38°C, input material 20°C, ambient 15°C, wind speed 5 m/s

SUB SUBH ,t

BG BGH ,t

DIG DIGH ,t

Reft

Reft

ReftReaktion

SUBH

BGH

DIGH

HeizQ LossQ DissP

Documents

Modelling of Material and Energy Balance of Biogas ... · PDF fileModelling of Material and Energy Balance of Biogas Production Process Mandy Gerber 2nd GERG Academic Network Event