A techno-economic overview of biomass based power generation with CO 2 capture technologies

Transcription

1 A techno-economic overview of biomass based power generation with CO 2 capture technologies Amit Bhave, George Brownbridge, Nicola Bianco and Jethro Akroyd CMCL Innovations CCS in the UK Moving Forward Manchester Biannual Apr 2016

2 Contents Context Analysing biomass conversion combined with CO 2 capture, utilisation and storage Virtual engineering toolkits for analysis MoDS : key features with examples Wrapping/coupling with 3 rd party toolkits Surrogates and parameter estimation Uncertainty propagation Application: Biomass-based power generation with CO 2 capture

3 Biomass conversion with CCS AR5 WGIII IPCC 2014 Unprecedented emphasis on development and deployment of technologies with negative CO 2 footprint to achieve below 450 ppm by 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, 2 nd and 3 rd 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

4 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

5 CMCL Innovations - Introduction Computational Modelling Cambridge Ltd. Innovation awards Business model Software Consulting Training Market segments Powertrains & fuels Energy & chemicals Simulation and design software supplier to industry and academia >10 years in innovative R&D and advanced engineering services Organically growing experienced team

6 Simulation toolkits kinetics & SRM Engine Suite Simplified design and application of chemical kinetics models to engineering applications Applications: Chemical kinetics: fuels, emissions pathways Chemical reactor design IC engine development MoDS: Model Development Suite Wraps around any software or script and offers advanced analysis Applications: Data standardisation and data-driven models Model calibration/parameter estimation Surrogates and sensitivity analysis Uncertainty propagation Physico-chemical Statistical

7 Contents Context Analysing biomass conversion combined with CO 2 capture, utilisation and storage Virtual engineering toolkits for analysis MoDS : key features with examples Wrapping/coupling with 3 rd party toolkits Surrogates and parameter estimation Uncertainty propagation Application: Biomass-based power generation with CO 2 capture

8 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

9 Selected features: generating surrogates Coupling with 3 rd party toolkits e.g., Fermenter model from gproms (PSE) Surrogates generated HDMR Sensitivities evaluated

10 Selected features: uncertainty analysis C-FAST bio-refinery example Global sensitivity of algal diesel production cost MoDS accounts for uncertainty in data propagating through to the plant and unit operation models CMCL Innovations. UK Patent office filing No

11 Contents Context Analysing biomass conversion combined with CO 2 capture, utilisation and storage Virtual engineering toolkits for analysis MoDS : key features with examples Wrapping/coupling with 3 rd party toolkits Surrogates and parameter estimation Uncertainty propagation Application: Biomass-based power generation with CO 2 capture

12 Application: BioPower CCS Acknowledgements Project partners and co-authors

13 Approach

14 BioPower CCS Technology landscape Post-combustion Oxy-combustion Pre-combustion Solvent scrubbing, e.g. MEA, chilled ammonia Low-temp solid sorbents, e.g. supported amines Ionic liquids Membrane separation Enzymes 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 Chemicalloopingcombustion 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 Coal IGCC gasification Direct cofiring Conversion to 100% biomass Not feasible Not feasible Pulverised coal combustion Direct cofiring Conversion to 100% biomass a a 13 Dedicated biomass combustion Fixed grate Bubbling fluidised bed Circulating fluidised bed a a Not feasible Bubbling fluidised bed 14 Dedicated biomass gasification Circulating fluidised bed Dual fluidised bed Not feasible Not feasible Entrained flow

15 Technology options selected Criteria Likely TRL in 2020 Key technical issues Suitability for small scale Plant efficiency with capture Capital costs with capture UK deployment potential 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 7 to 8 6 to to 6 5 to to 6 Scale-up, amine degradation, Scale-up, amine degradation, O 2 energy costs, slow response O 2 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 Low High Low High Low High Low High OK Low OK Low Good Good High, Good OK Expensive OK High ASU costs Immediate capture retrofit opportunities, retrofit opportunities high longterm potential retrofit opportunities, long-term doubtful retrofit opportunities, high longterm potential OK OK OK Expensive, 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 High longterm potential

16 Approach with an example: Bio chem loop Input Samples u TRL: Technology Readiness Levels y Outputs; Meta- Model generation Metamodel Case studies (WP2), Public domain data/models

17 BioPower CCS at base scales Process engineering output:

18 BioPower CCS at 50 MW e Plant-wide techno-economic model parameter estimation: CAPEX, OPEX, LHV efficiency and emissions as a function of scale, co-firing and extent of capture

19 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 ( /MW e ) with CO 2 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 MW e ), intermediate (250 MW e ) and large (~600 MW e ) scales. At large scales, the issue of sustainable biomass procurement and LUC need careful consideration. Incentivising negative CO 2 emissions via the capture and storage of biogenic CO 2 under the EU emissions trading scheme (ETS) is highly important.