DEVELOPMENT OF NANO-STRUCTURED MATERIALS THROUGH A NOVEL MULTI-SCALE - - PowerPoint PPT Presentation

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DEVELOPMENT OF NANO-STRUCTURED MATERIALS THROUGH A NOVEL MULTI-SCALE - - PowerPoint PPT Presentation

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DEVELOPMENT OF NANO-STRUCTURED MATERIALS THROUGH A NOVEL MULTI-SCALE MODELLING FRAMEWORK FOR ENERGY CONVERSION WITH CO 2 CAPTURE Shareq Mohd Nazir 1,* , Joana Francisco Morgado 1,2,5 , Stefan Andersson 3 , Zheng Xiao Guo 4 , Shahriar Amini 1,3 1

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Norwegian University of Science and Technology

DEVELOPMENT OF NANO-STRUCTURED MATERIALS THROUGH A NOVEL MULTI-SCALE MODELLING FRAMEWORK FOR ENERGY CONVERSION WITH CO2 CAPTURE

Shareq Mohd Nazir1,*, Joana Francisco Morgado1,2,5, Stefan Andersson3, Zheng Xiao Guo4, Shahriar Amini1,3

1 Norwegian University of Science and Technology, Trondheim, Norway 2 University of Coimbra, Coimbra, Portugal 3 SINTEF Industry, Trondheim, Norway 4 University College London, United Kingdom 5 Ifavidro Lda, Portugal

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Norwegian University of Science and Technology

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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Background

Source: International Energy Agency (2017), Energy Technology Perspectives 2017, OECD/IEA, Paris

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CO2 capture methods

Fuel (coal, natural gas, oil, biomass) Power Plant CO2 Capture CO2 conditioning, transport and storage Gasification / Reforming Shift CO2 Capture Power Plant Air separation unit Power Plant Water removal Exhaust CO2 CO2 CO2 Syngas H2 O2 Air

Post- combustion Pre-combustion Oxy-combustion

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Reforming methods

Steam Methane Reforming (SMR) Partial Oxidation (POX) Chemical Looping Reforming (CLR) SMR 𝐷𝐼4 + 𝐼2𝑃 ⇌ 𝐷𝑃 + 𝐼2 𝐷𝐼4 + 𝑃2 ⇌ 𝐷𝑃 + 𝐼2 𝐷𝐼4 + 𝑁𝑓𝑃 ⇌ 𝐷𝑃 + 𝐼2 POX CLR

  • CLR has less thermodynamic losses and has inherent air

separation

  • CLR reforms CH4 to a product gas with higher H2/CO ratio

when compared to conventional POX

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Project NanoSim: A Multiscale Simulation-Based Design Platform for Cost-Effective CO2 Capture Processes using Nano-Structured Materials (EU FP7 framework)

  • 1. System Scale
  • 2. Equipment

Scale

  • 3. Cluster Scale
  • 4. Particle Scale
  • 5. Intra-particle

pore scale

  • 6. Atomistic scale

Earlier project

Consortium 1. SINTEF Industry 2. TU Graz 3. University College London 4. INPT Toulouse 5. NTNU 6. DCS Computing GmbH 7. Andritz Energy and Environment GmbH 8. University de Coimbra

https://www.sintef.no/projectweb/nanosim/

  • Develop an open-source computational platform that will

allow the rational design of the second generation of gas-particle CO2 capture technologies based on nano- structured materials

  • Design and manufacture nano-structured material and

shorten the development process of nano-enabled products based on the multi-scale modelling

  • Design and demonstrate an energy conversion reactor

with CO2 capture based on the superior performance of nano-structured materials

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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Atomistic and cluster scale modeling

  • Reactivity of nanoparticles at the atomic scale/nanoscale, is estimated through kinetic

Monte Carlo (kMC) modeling, guided by Density Functional Theory (DFT) calculations, on the detailed kinetics of the CH4 conversion to products as a function

  • f temperature.
  • Cluster scale:
  • Intra-particle transport model
  • Fluid-Particle flow model (Tools: LIGGGHTS for particle motion and CFDEM for

fluid flow)

Reference: Andersson, S., et al., Towards rigorous multiscale flow models of nanoparticle reactivity in chemical looping

  • applications. Catalysis Today, 2019.
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Equipment scale - 1D Model of CLR

  • Rapid convergence
  • Wide range of applicability (reasonably generic)
  • User friendly
  • Accommodate reactor clusters
  • Handle dynamic and stationary simulations

“Generalized fluidized bed reactor” (GFBR) model

Bubbling Turbulent Fast fluidization

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Generic formulation based on the generic model developed by Abba et al. (2003)

  • uses an averaging probabilistic approach
  • Two-phase model

Reference: Abba, I.a., et al., Spanning the flow regimes: Generic fluidized-bed reactor model. AIChE Journal, 2003. 49: p. 1838-1848.

Single formulation is used!

  • Mass balance
  • Gas total mass balance
  • Gas species mass balance for

each phase

  • Total solids species mass

balance

  • Total Energy balance
  • Pressure Balance

Differential Balances

Numerical scheme:

  • Method of lines (MATLAB routine ode15s)
  • Finite volume method (discretization in

space)

  • Non-uniform grid
  • Convective term: 1st order upwind

scheme

  • Diffusion term: central differences

scheme

1-D model for fluidized bed reactors

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Two phases

H-Phase L-Phase ψ𝑀, 𝑣𝑀, 𝜁𝑀, 𝐸𝑕,𝑀 ψ𝐼, 𝑣𝐼, 𝜁𝐼, 𝐸𝑕,𝐼

𝐿

Sketch of the two-phase approach for a fluidized bed reactor MeO Me Air reactor Fuel reactor Air CH4 + H2O Depleted air Syngas (CO+H2) + CO2 + H2O

𝑈𝑡,𝐺𝑆 𝑥𝑡,𝐺𝑆 𝐻𝑡,𝐺𝑆 𝑈𝑡,𝐵𝑆 𝑥𝑡,𝐵𝑆 𝐻𝑡,𝐵𝑆

Simulation domain

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Averaging probabilistic approach

Bubbling Regime j=1 Turbulent Regime j=2 Fast Fluidization Regime j=3

𝑸𝒌 Probability of being under regime j 𝜄1 𝜄2 𝜄3 𝜄 = 𝑸𝟐𝜄1 + 𝑸𝟑𝜄2 + 𝑸𝟒𝜄3

  • Library of closures for different fluidization regimes

Reference: Abba, I.a., et al., AIChE Journal, 2003. 49: p. 1838-1848.

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1D Model outline

Differential Balances Mass balance; Energy balance; Pressure Constants Reactor dimensions; Fundamental and kinetic cosnstants Thermochemical properties Relations for gas and solids properties Reaction kinetics Closures for hydrodynamics Bubbling, Turbulent and Fast Fluidization Regimes Probabilistic Approach Define the model hydrodynamic parameters Solver Simulation results

Initial and Boundary conditions

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KMC – Kinetic Monte Carlo

  • Kinetic parameters (Arrehnius parameters)

Gas physical properties/conditions

  • Flowrate
  • Density
  • Composition
  • Heat capacity

Solid physical properties/conditions

  • Flowrate
  • Density
  • Composition
  • Temperature
  • Heat capacity
  • Particle size

Affects: Gas velocities Void fraction Temperature Reaction rate (R = kCn) Pressure drop Affects: Solids velocities Void fraction Temperature Reaction rate (R = kCn) Pressure drop Solid recirculation rate

Parameter interaction in 1D-Model

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System (process plant) scale model

MATLAB Thermodynamics Hydrodynamics + Kinetics Thermodynamics

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Interaction between 1d model and plant scale simulations

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Pre-combustion combined cycle with CLR (CLR-CC)

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Key performance indicators

CO2 Capture (%) CO2 Avoidance (%) Net Electrical Efficiency (%-LHV input) =

𝐷𝑃2 𝐷𝑏𝑞𝑢𝑣𝑠𝑓𝑒 𝐷𝑃2 𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑓𝑒 𝑗𝑜 𝑢𝑖𝑓 𝑞𝑠𝑝𝑑𝑓𝑡𝑡 × 100

=

𝐷𝑃2 𝑓𝑛𝑗𝑢𝑢𝑓𝑒 𝑐𝑧 𝑠𝑓𝑔. 𝑞𝑚𝑏𝑜𝑢 −𝐷𝑃2 (𝑓𝑛𝑗𝑢𝑢𝑓𝑒 𝑐𝑧 𝑞𝑠𝑝𝑑𝑓𝑡𝑡) 𝐷𝑃2 (𝑓𝑛𝑗𝑢𝑢𝑓𝑒 𝑐𝑧 𝑠𝑓𝑔. 𝑞𝑚𝑏𝑜𝑢)

× 100 =

𝑂𝑓𝑢 𝐹𝑚𝑓𝑑𝑢𝑠𝑗𝑑𝑗𝑢𝑧 𝑄𝑠𝑝𝑒𝑣𝑑𝑓𝑒 𝑀𝐼𝑊 𝑝𝑔 𝑔𝑣𝑓𝑚 𝑗𝑜𝑝𝑣𝑢 𝑢𝑝 𝑞𝑠𝑝𝑑𝑓𝑡𝑡 × 100

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Key performance indicators

*GCCSI. 2013. Global CCS Institute - TOWARD A COMMON METHOD OF COST ESTIMATION FOR CO2 CAPTURE AND STORAGE AT FOSSIL FUEL POWER PLANTS.

𝑀𝐷𝑃𝐹 = (𝑈𝐷𝑆)(𝐺𝐷𝐺) + 𝐺𝑃𝑁 (𝑁𝑋)(𝐷𝐺 × 8766) + 𝑊𝑃𝑁 + (𝐼𝑆)(𝐺𝐷)

Levelised cost of electricity ($/MWh)

TCR – Total capital requirement FOM – Fixed operating & maintenance costs FC – Fuel costs VOM – Variable operating & maintenance costs HR – Heat Rate

Cost of CO2 avoided

𝐷𝑝𝑡𝑢 𝑝𝑔 𝐷𝑃 𝑏𝑤𝑝𝑗𝑒𝑓𝑒 $ 𝑢𝐷𝑃2 ) = 𝑀𝐷𝑃𝐹𝐷𝑀𝑆 − 𝐷𝐷 − 𝑀𝐷𝑃𝐹𝑂𝐻𝐷𝐷 𝑢𝐷𝑃2 𝑁𝑋ℎ 𝑂𝐻𝐷𝐷 − 𝑢𝐷𝑃2 𝑁𝑋ℎ 𝐷𝑀𝑆 − 𝐷𝐷

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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Flow of data

Environment and Market

  • Kinetic data from

atomic/molecular simulations

  • Particle size and

shape Physical phenomenological modeling at equipment scale with closures derived at atomic/cluster level

  • Heat transfer
  • Mass transfer
  • Hydrodynamics
  • Reactions

Process modeling and simulation by linking the equipments together

  • Thermodynamics
  • Process integration
  • Process efficiency
  • Optimization
  • Cost of electricity
  • CO2 captured and avoided
  • Cost of CO2 avoided

Physics and Chemistry Chemical Engineering Process Systems Engineering Economics Atomic/Particle Scale Equipment Scale Plant Scale Global Scale

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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Support particle size: 250 microns

Conversion profiles in CLR – 1D Model

Base case kinetic data from literature Assuming 50x times faster kinetics Installed cost of CLR = 49 M€ Installed cost of CLR = 41 M€

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Sensitivity study for techno-economic analysis

Cases O2/CH4 by moles Steam/CH4 by mass Oxidation Reactor Outlet Temperature (°C) CH4 flow (TPH) 1 0.9 0.5 1200 170 2 0.9 1 1200 170 3 0.9 1.5 1200 172 4 0.9 0.5 1100 170 5 0.9 1 1100 170 6 0.9 1.5 1100 170 7 0.8 0.5 1200 160 8 0.8 1 1200 160 9 0.8 1.5 1200 160 10 0.8 0.5 1100 160 11 0.8 1 1100 160 12 0.8 1.5 1100 160

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Techno-economic performance

20 40 60 80 100 120 140 160 1 2 3 4 5 6 7 8 9 10 11 12

Split of LCOE ($/MWh) Cases

LCOE

TCR FOM VOM FC

*Nazir, S.M., et al., Techno-economic assessment of chemical looping reforming of natural gas for hydrogen production and power generation with integrated CO2 capture. International Journal of Greenhouse Gas Control, 2018. 78: p. 7-20

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Oxygen carrier related costs

5000 10000 15000 20000 25000 1 2 3 4 5 6 7 8 9 10 11 12

Oxygen carrier flowrate (TPH)

Oxygen carrier flowrate at different Steam/CH4 ratio (mass) in the fuel reactor of CLR

Steam/CH4 = 0.5 Steam/CH4 = 1 Steam/CH4 = 1.5

Oxygen carrier flow in case 1 = 12289 TPH Lifetime: 5 years Variable O&M cost from oxygen carrier ~1.4 €/MWh Lifetime: 0.5 years Variable O&M cost from oxygen carrier ~ 14 €/MWh *Considering cost of Ni-NiO

  • xygen carriers

*Nazir, S.M., et al., Techno-economic assessment of chemical looping reforming of natural gas for hydrogen production and power generation with integrated CO2 capture. International Journal of Greenhouse Gas Control, 2018. 78: p. 7-20

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Outline

  • Background and introduction
  • Different modeling scales

– Atomistic level modeling – 1D modeling (reactor scale) – Process modeling (plant scale)

  • Description of the method – flow and type of data
  • Results
  • Summary
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Summary

  • A method to develop oxygen carrier materials for chemical looping systems from a

techno-economic perspective is discussed.

  • The method aims to reduce the time required to test different materials

experimentally.

  • The tools at atomic, equipment and plant scale have been developed and tested.
  • Future work will focus on mapping techno-economic process peformance with

different material properties. This chart could then be used a starting point to consider

  • xygen carrier materials for respective chemical looping systems.
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Opportunities

Environment and Market

  • Kinetic data from

atomic/molecular simulations

  • Particle size and

shape Physical phenomenological modeling at equipment scale with closures derived at atomic/cluster level

  • Heat transfer
  • Mass transfer
  • Hydrodynamics
  • Reactions

Process modeling and simulation by linking the equipments together

  • Thermodynamics
  • Process integration
  • Process efficiency
  • Optimization
  • Cost of electricity
  • CO2 captured and avoided
  • Cost of CO2 avoided

Physics and Chemistry Chemical Engineering Process Systems Engineering Economics Atomic/Particle Scale Equipment Scale Plant Scale Global Scale

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Thank you

Shareq.m.nazir@ntnu.no