Models discretization utilizes Pyomo.DAE with an orthogonal collocation scheme

BFB-CLC: 16,484 variables and 16,476 equations

MB-CLC: 20,789 variables and 20,781 equations

Models are solved using IPOPT

Develop dynamic models

Extend models to systems with multi-solid phases i.e. coal combustion with CLC

Validate models with pilot-scale data

Design and Optimization of Chemical Looping Combustion ProcessesChinedu Okolia, Anca Ostaceb, Andrew Leea, Anthony Burgarda, Debangsu Bhattacharyyab, David Millera

a National Energy Technology Laboratory, bWest Virginia University

• Advanced combustion system with inherent CO2 separation

• Potential cost savings compared with conventional power technologies with CO2 capture systems

• A fuel reactor is coupled to an air reactor with a solid oxygen carrier (OC) undergoing reduction-

oxidation reactions as it circulates between the reactors

• Large-scale modeling & optimization tools can accelerate CLC technology

• Identify optimal operating and design conditions

• Guide design of experiments at bench and pilot-scales

• Identify synergies and trade-offs between unit-operations and operating conditions in plant-scale

CLC processes

• Models must capture both micro and macro-scale phenomena

• Micro-scale phenomena such as the effect of OC particle characteristics on reaction, mass and

heat transfer

• Macro-scale phenomena such as effect of reactor geometry, and OC circulation rate on process

operating and capital costs

IDAES Bubbling Fluidized Bed (BFB) Reactor Model

Chemical Looping Combustion (CLC)

• Unit Operation models required to represent Advanced Energy Systems such as

CLC are typically complicated and not present in existing commercial process

systems engineering software

• IDAES model library contains solid-fluid contactors which capture both micro-

and macro-scale phenomena, essential for accurate modeling of CLC

• Models are “glass box”, allowing customization for specific user needs

• Models are multi purpose:

• Built for both simulation and optimization purposes

• Built for both steady-state and dynamic operations

• Modular architecture allows flexibility in building large scale optimization

flowsheets

• Open Source enables customization to meet specific requirements of novel

systems.

Why IDAES for CLC Process Design & Optimization?CLC Flowsheet

IDAES Moving Bed (MB) Reactor Model

Optimization Formulation

Objective

Minimize TAC – reactors cost, OC inventory, gas compression costs etc.

Decision Variables

Size of beds – diameter, height

Oxygen carrier circulation rate

Air flowrate

Constraints

Model equations (Fuel reactor & Air reactor)

Fuel and Air temperature

Pressure of reactors

Operating constraints

Future workModel Statistics

BFB-CLC Results MB-CLC Results

Base case

(heuristic design)

simulation

Optimal design

FR AR FR AR

Design variables:

Bed diameter (m) 8 12 4.6 12.6

Bed height (m) 5 4 4.6 2.4

Operating variables:

OC in flowrate (kg/s) 591 - 549 -

Gas in flowrate (mol/s) - 1,587 - 1,199

Gas in pressure (kPa) 137 137 250 101

Conversions:

Methane_FR (%) 100 - 100 -

OC_AR (%) - 99.8 - 99.8

Heuristic vs. optimal design parameters,

operating conditions and conversionsBase case

(heuristic design)

simulation

Optimal design

FR AR FR AR

Design variables:

Reactor diameter (m) 6.5 14.9 10.6 13.3

Reactor height (m) 5 5 5.3 6.6

Operating variables:

OC in flowrate (kg/s) 1,422 - 1,078 -

Gas in flowrate (mol/s) - 1,429 - 1,473

Gas in pressure ( kPa) 156 156 234 440

Gas in temperature (K) 298 298 584 298

Conversions:

Methane_FR (%) 73 - 97 -

OC_AR (%) - 92 - 99

Heuristic vs. optimal design parameters,

operating conditions and conversions

This presentation was prepared as an account of work sponsored by an agency of the United States Government. Neither

the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied,

or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus,

product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any

specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily

constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.

The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States

Government or any agency thereof

Disclaimer

CLC Case Studies

Optimal design of a 100 MWth natural gas CLC process using interconnected MB

and BFB reactors, with an iron-based oxygen carrier 1

Fuel reactor: CH4(g) + 12Fe2O3(s) → CO2(g) + 2H2O(g) + 8Fe3O4(s) Air reactor: 2O2(g) + 8Fe3O4(s) → 12Fe2O3(s)2. Okoli C.O., Ostace A., Nadgouda S., Lee A., Tong A., Burgard A.P., Bhattacharyya D., and Miller D.C., 2018, “A Framework for the optimization

of Chemical Looping Combustion Processes”, Journal of Powder Technology (under review)

• Model Equations Mass balance equations

Energy balance equations

Pressure balance

Hydrodynamic behavior

• 1-D, counter-current steady-state MB model, with different

operating modes: isothermal, adiabatic, and non-isothermal 3

First principles glass-box model, with automated

sequential initialization

Suitable for simulation and optimization

Modular & flexible – readily adaptable for the simulation

of different processes

3. Ostace A., Lee A., Okoli C. O., Burgard A. P., Miller D. C., Bhattacharyya D., Proceedings of the 13th International Symposium on Process Systems

Engineering (PSE 2018), Computer-Aided Chemical Engineering, 44, pp., 325-330, 2018.

Sample CLC flowsheet of interconnected Moving Bed reactors

Emulsion

region

Bubble region

Cloud-Wake

region

Gas in

Gas out Solid in

Solid out

• 1-D, three-region steady-state model 1

Gas Flow

Solid Flow

•Model features First principles mass and energy balance equations

Empirical pressure drop and bed hydrodynamic correlations

Heat and mass transfer correlations

Well mixed gas and solids in radial direction

Flexible gas & solid feed and exit locations1. Okoli C. O., Lee A., Burgard A. P., Miller D. C., Proceedings of the 13th International Symposium on Process Systems Engineering (PSE 2018),

Computer-Aided Chemical Engineering, 44, pp., 259-264, 2018.

Gas phase properties – diffusion, viscosity, thermal conductivity

Solid phase properties – particle size distribution, density, sphericity,

minimum fluidization velocity & porosity, terminal velocity, thermal conductivity

Thermodynamics – heat of formation, heat capacity, equation of state

Kinetics – Shrinking core kinetic model for

spherical grain geometry 4

CLC Property Package

𝑑𝑋𝑠𝑑𝑡

=3𝑏𝑘𝐶𝑔

𝑛

𝜌𝑚𝑟𝑔1 − 𝑋𝑠

2/3

4. Abad A., et al., Mapping of the range of operational conditions for Cu-, Fe-, and Ni-

based oxygen carriers in CLC, Chem. Eng. Sci. 62 (2007) 533–549.

% Change in costs for varying OC prices

(500 $/t [dark bars], 2,000 $/t [grey bars]),

relative to the optimal design (1,300 $/t)

% Change in costs for varying OC prices

(500 $/t [dark bars], 2,000 $/t [grey bars]),

relative to the optimal design (1,300 $/t)