a techno-economic overview of biomass based power ... · biomass with amine scrubbing co-firing...
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A techno-economic overview of biomass based power generation with CO2 capture technologies
Amit Bhave, George Brownbridge, Nicola Bianco and Jethro Akroyd
CMCL Innovations
13-14 Apr 2016
CCS in the UK – Moving ForwardManchester Biannual
Part I | Part II| Part III
Contents
• Context– Analysing biomass conversion combined with CO2 capture, utilisation and
storage
– Virtual engineering toolkits for analysis
• MoDS™: key features with examples– Wrapping/coupling with 3rd party toolkits
– Surrogates and parameter estimation
– Uncertainty propagation
• Application: Biomass-based power generation with CO2 capture
Part I | Part II| Part III
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, negativeemissions, 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
• TESBiC 2011/2012: BioPower CCS - key technical and economic barriers, and UK deployment potential to 2050
Part I | Part II| Part III
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
CMCL Innovations - Introduction
Computational Modelling Cambridge Ltd.
Business model
Software | Consulting | Training
Market segments
• Powertrains & fuels
• Energy & chemicals
Innovation awards
www.cmclinnovations.com
Simulation and design software supplier to industry and academia
>10 years in innovative R&D and advanced engineering services
Organically growing experienced team
Part I | Part II| Part III
Simulation toolkits
MoDS: Model Development Suite
kinetics & SRM Engine Suite
“Wraps around any software or script and offers advanced analysis”
“Simplified design and application of chemical kinetics models to engineering applications”
Applications: • Chemical kinetics: fuels, emissions pathways• Chemical reactor design• IC engine development
Applications: • Data standardisation and data-driven models • Model calibration/parameter estimation• Surrogates and sensitivity analysis• Uncertainty propagation
Ph
ysico-ch
emical
Statistical
Part I | Part II| Part III
Contents
• Context– Analysing biomass conversion combined with CO2 capture, utilisation and
storage
– Virtual engineering toolkits for analysis
• MoDS™: key features with examples– Wrapping/coupling with 3rd party toolkits
– Surrogates and parameter estimation
– Uncertainty propagation
• Application: Biomass-based power generation with CO2 capture
Part I | Part II| Part III
MoDS™
MODS is a unique software tool which 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 and local sensitivity analysis
(h) Intelligent design of experiments
Part I | Part II| Part III
Selected features: generating surrogates
• Coupling with 3rd party toolkits – e.g., Fermenter model from gPROMS™ (PSE)
• Surrogates generated – HDMR
• Sensitivities evaluated
Part I | Part II| Part III
Selected features: uncertainty analysis
• C-FAST bio-refinery example
MoDS accounts for uncertainty in data propagating through to the plant and unit operation models
Global sensitivity of algal diesel production cost
CMCL Innovations. UK Patent office – filing No. 1118696.2
Part I | Part II| Part III
Contents
• Context– Analysing biomass conversion combined with CO2 capture, utilisation and
storage
– Virtual engineering toolkits for analysis
• MoDS™: key features with examples– Wrapping/coupling with 3rd party toolkits
– Surrogates and parameter estimation
– Uncertainty propagation
• Application: Biomass-based power generation with CO2 capture
Part I | Part II| Part III
Application: BioPower CCS
• Acknowledgements
• Project partners and co-authors
Part I | Part II| Part III
Approach
Part I | Part II| Part III
BioPower CCS – Technology landscape
Solvent
scrubbing,
e.g. MEA,
chilled
ammonia
Low-temp
solid
sorbents,
e.g.
supported
amines
Ionic
liquidsEnzymes
Membrane
separation
of CO2 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 H2 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 106a
Post-combustion Oxy-combustion Pre-combustion
Coal IGCC
gasificationNot feasible Not feasible 15 17 19 21 23
Pulverised coal
combustion1 3 5 75a
Part I | Part II| Part III
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
2020Long-term
doubt
No current UK plants,
demo unlikely by
2020.High long-
term potential
Part I | Part II| Part III
Approach with an example: Bio chem loop
TRL: Technology Readiness Levels
Input Samples
Outputs; Meta-Model
generationu
yMeta-model
Case studies (WP2),Public domain data/models
Part I | Part II| Part III
BioPower CCS at base scalesProcess engineering output:
Part I | Part II| Part III
BioPower CCS at 50 MWe
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
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.