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Transcript of Biomass pre-treatment and conversion combined …. ©CMCL Innovations, 2016 Biomass pre-treatment...
Confidential. ©CMCL Innovations, 2016
Biomass pre-treatment and conversion combined with CO2 capture storage and utilisation technologies
Amit Bhave, George Brownbridge, Nicola Bianco and Jethro Akroyd CMCL Innovations
23 Jun 2016
BECCS Specialist Meeting Imperial College, London
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Contents
• Context – Biomass conversion combined with CO2 capture, utilisation and storage – Virtual engineering toolkits for analysis
• MoDS™: key features
– Wrapping/coupling with 3rd party toolkits – Surrogates and parameter estimation
• Application 1: Micro-algal biomass conversion to hydrocarbon fuels
• Application 2: Biomass-based power generation with CO2 capture
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Biomass conversion with CCS
• AR5 WGIII IPCC 2014 – Unprecedented emphasis on development and deployment of technologies with negative CO2 footprint to achieve below 450 ppm by 2100.
• ETI’s ESME toolkit’s least-cost options for meeting UK’s energy demand and emissions targets to 2050, identify biomass CCS as vital with large, negative emissions, a high option value and high persistence
• IEAGHG, 2011: Despite its strong GHG reduction potential, there is a considerable dearth of information for biomass CCS as compared to fossil CCS
• APGTF, 2011/2012: RD&D strategic themes and priorities - whole system : focus on virtual system simulation and optimisation - capture technologies: focus on economics, efficiency penalty, co-fired biomass,
2nd and 3rd generation technologies
• EBTP/ZEP 2012: Accelerate deployment of advanced biomass conversion processes
Biomass CCS includes Biopower and Biofuels
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Virtual engineering analysis for Biomass CCS
• System-level analysis – Life cycle analysis – Process systems engineering
• Component-level analysis – Multi-dimensional CFD – 0/1D reactor models – Chemical kinetic schemes
• Measurements data • Data-driven models • Model-based optimal Design
of Experiments (DoE) • Optimisation • Reduced-order or surrogates • Uncertainty analysis
Biomass CCS includes Biopower and Biofuels
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
MoDS™ Model Development Suite (MODS) can be “wrapped around” any process, system or software, enabling: (a) Data-driven modelling
(b) Rapid multi-objective optimisation of processes, systems, technologies
(c) The generation of surrogates (fast response) models derived from more complex
systems/processes. e.g. Polynomial fits, High dimensional model representation (HDMR)
(d) Data standardisation and visualisation
(e) Global parameter estimation for all models
(f) Uncertainty propagation throughout systems
(g) Global sensitivity analysis
(h) design of experiments
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Selected features: coupling with 3rd party toolkits
gPROMS model
g-PROMS executable (encrypted)
xml input file (runSimulation.xml)
input (folder)
gORUN_xml.exe xml output file (output.xml)
Parameters/variables sweeping (Sobol/Monte
Carlo)
Surrogate modelling (HDMR, polynomial approximation)
Optimal parameters/variables estimation
MoDS Global sensitivity analysis and uncertainty propapation
Design of experiments
Coupling with 3rd party toolkits: e.g., gPROMS™ (PSE)
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Selected features: generating surrogates
• e.g., Fermenter model from gPROMS™ (PSE) • Surrogates generated – HDMR • Sensitivities evaluated
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Application 1: micro-algal biomass to fuels
UK Patent office – filing No. 1118696.2
C-FAST biorefinery
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Process flowsheet model
CSP
PBR
Astaxanthin
Transesterification
C-FAST biorefinery
Gasification + Fischer-Tropsch
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Selected features: uncertainty propagation • C-FAST bio-refinery example
MoDS accounts for uncertainty in data propagating through to the plant and unit operation models
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Selected features: sensitivity analysis • C-FAST bio-refinery example
Global sensitivity of algal diesel production cost
Global sensitivity of ROI to inputs, (a) 5 and (b) 30 years
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Application 1 - Summary
• Global sensitivities take into account the input parameter variation range
• Sensitivity of the production cost of algal-derived Diesel decreases in the following order: – algal oil content – algal specific annual productivity – plant capacity – Carbon price increase rate – PBR unit CAPEX
• Crude oil and carbon price increase rates influence long term ROI substantially, as compared to the negligible impact on short-term ROI.
• Plant solely producing algal biodiesel not commercially feasible; needs
supplementary revenue from producing additional value-added products.
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Application 2: BioPower CCS • Acknowledgements
• Project partners and co-authors
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Approach
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
BioPower CCS – Technology landscape
Solvent scrubbing, e.g. MEA, chilled
ammonia
Low-temp solid
sorbents, e.g.
supported amines
Ionic liquids Enzymes
Membrane separation of CO 2 from
flue gas
High-temp solid
sorbents, e.g.
carbonate looping
Oxy-fuel boiler with
cryogenic O2 separation
Oxy-fuel boiler with membrane
O2 separation
Chemical- looping-
combustion using solid
oxygen carriers
IGCC with physical
absorption e.g.
Rectisol, Selexol
Membrane separation of H 2 from synthesis
gases
Membrane production of syngas
Sorbent enhanced reforming
using carbonate
looping
ZECA concept
Direct cofiring
Conversion to 100% biomass
Direct cofiring
Conversion to 100% biomass
Fixed grate
Bubbling fluidised bed
Circulating fluidised bed
Bubbling fluidised bed
Circulating fluidised bed
Dual fluidised bed
Entrained flow
22 24
12
14
9 11 13
Not feasible
18 20
11a
12a
Not feasible Dedicated biomass gasification
Not feasible 16
Dedicated biomass combustion
2 4 6 8 10 6a
Post-combustion Oxy-combustion Pre-combustion
Coal IGCC gasification Not feasible Not feasible 15 17 19 21 23
Pulverised coal combustion 1 3 5 7 5a
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Technology options selected
Criteria
Co-firing amine
scrubbing
Dedicated biomass with
amine scrubbing
Co-firing oxy-fuel
Dedicated biomass oxy-fuel
Co-firing carbonate looping
Dedicated biomass chemical looping
Co-firing IGCC
Dedicated biomass BIGCC
Likely TRL in 2020
7 to 8 6 to 7 7 6 5 to 6 5 to 6 7 5 to 6
Key technical issues
Scale-up, amine
degradation,
Scale-up, amine
degradation,
O2 energy costs, slow response
O2 energy costs, slow
response
Calciner firing, solid degradation, large purge of CaO
Loss in activity, reaction
rates, dual bed
operation
Complex operation,
slow response, tar
cleaning, retrofit
impractical
Complex operation,
slow response, tar
cleaning, retrofit
impractical Suitability for small scale
Low High Low High Low High Low High
Plant efficiency with capture
OK Low OK Low Good Good High, Good
Capital costs with capture
OK Expensive OK High ASU costs
OK OK OK Expensive,
UK deployment potential
Immediate capture retrofit
opportunities,
retrofit opportunities
high long-term
potential
retrofit opportunities
, long-term doubtful
retrofit opportunities
, high long-term
potential
capture retrofit opportunities,
cement integration
Likely first demos in
Europe, UK in ~2020. High long term potential
No current UK plants,
several demos by
2020 Long-term
doubt
No current UK plants,
demo unlikely by
2020. High long-
term potential
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Approach with an example: Bio chem loop
TRL: Technology Readiness Levels
Input Samples
Outputs; Meta-Modelgeneration
u yMeta-model
Case studies (WP2),Public domain data/models
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
BioPower CCS at base scales Process engineering output:
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
BioPower CCS at 50 MWe – Efficiency vs CAPEX
Plant-wide techno-economic model parameter estimation: CAPEX, OPEX, LHV efficiency and emissions as a function of scale, co-firing and extent of capture
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
BioPower CCS at 50 MWe – LCOE
Levelised cost of electricity (LCOE)
Coal: £7/MWh Chips: £10/MWh Pellets: £27/MWh
40
60
80
100
120
140
160
180
200
220
240
2010 2020 2030 2040 2050
LCO
E (£
/MW
h e)
Cofire amine
Bio amine
Cofire oxy
Bio oxy
Cofire carb loop
Bio chem loop
Cofire IGCC
Bio IGCC
Part I | Part II| Part III Confidential. ©CMCL Innovations, 2016
Application 2 - Summary
• MoDS™ toolkit combined with process systems engineering applied to screen and analyse biomass (includes biopower and biofuels) CCS technologies
• For BioPower CCS, to date, setbacks from cancellation of planned projects and little activity at industrial scale
• For the eight BioPower CCS technologies varying over a wide range of current TRLs, from TRL 4
to TRL 7, the range of techno-economic parameters are the following: • ~ 6% to 15% : Range of the efficiency drop • ~ 45% to 130%: Range of the increase in specific CAPEX (£/MWe)
with CO2 capture • ~ 4% to 60%: Range of increase in OPEX (£/yr) with carbon capture
• CAPEX, LCOE: Generation scales and fuel costs the main drivers
• BioPower CCS attractive for small (50 MWe), intermediate (250 MWe) and large (~600 MWe)
scales. At large scales, the issue of “sustainable biomass procurement” and LUC need careful consideration.
• Incentivising negative CO2 emissions via the capture and storage of biogenic CO2 under the EU emissions trading scheme (ETS) is highly important.