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.