For Accelerating Technology Development

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2 For Accelerating Technology Development Rapidly synthesize optimized processes to identify promising concepts Better understand internal behavior to reduce time for troubleshooting Quantify sources and effects of uncertainty to guide testing & reach larger scales faster Stabilize the cost during commercial deployment National Labs Academia Industry Advisory Board 2

3 Goals & Objectives of CCSI ( ) Develop new computational tools and models to enable industry to more rapidly develop and deploy new advanced energy technologies Base development on industry needs/constraints Emphasis on supporting post-combustion capture technologies Limited work on oxycombustion system General tools capable of supporting all types of carbon capture technology development Demonstrate the capabilities of the CCSI Toolset on non-proprietary case studies Examples of how new capabilities improve ability to develop capture technology Deploy the CCSI Toolset to industry Collaborate with industry to use the tools to support technology development & scale up 3

4 CCSI Toolset: New Capabilities for Modeling & Simulation Maximize the learning at each stage of technology development Early stage R&D Screening concepts Identify conditions to focus development Prioritize data collection & test conditions Pilot scale Ensure the right data is collected Support scale-up design Demo scale Design the right process Support deployment with reduced risk 2016 R&D 100 Award 4 4

5 CCSI Toolset Module Connectivity Process Models CFD Device Models Solid In Gas Out Utility Out Process Synthesis, Design & Optimization Carbon Capture Process 24 CYC-001 S3 SHX ADS CPT Gas In Solid Out Utility In Legend Flue Gas Clean Gas Rich CO2 Gas Lean Sorbent Rich Sorbent LP/IP Steam HX Fluid S1 ELE-001 ELE-002 RGN-001 S4 CPP Injected Steam Cooling Water 7 CPR-002 GHX GHX-002 CPR-001 S SHX-002 S5 14 Basic Data Submodels Parallel ADS Units S Process Dynamics and Control 5

6 High Fidelity Process Models for Carbon Capture Bubbling Fluidized Bed (BFB) Model Variable solids inlet and outlet location Modular for multiple bed configurations Moving Bed (MB) Model Unified steady-state and dynamic Heat recovery system yb (kmol/kmol) CO2 H2O z/l Compression System Model Integral-gear and inline compressors Determines stage required stages, intercoolers Based on impeller speed limitations Estimates stage efficiency Off-design and surge control Solvent System Model Predictive, rate-based models kmol/hr Solid Component Flow Profile of MB Regenerator Bicarb. Carb. H 2 O z/l Membrane System Model Hollow fiber Supports multiple configurations 6

7 Framework for Optimization, Quantification of Uncertainty and Surrogates ALAMO Surrogate Models Simulation Based Optimization Heat Integration UQ Optimization Under Uncertainty D-RM Builder ireveal Surrogate Models Data Management Framework Samples FOQUS Framework for Optimization Quantification of Uncertainty and Surrogates Meta-flowsheet: Links simulations, parallel execution, heat integration Results SimSinter Config GUI Turbine Parallel simulation execution management system Desktop Cloud Cluster SimSinter Standardized interface for simulation software Steady state & dynamic Simulation Aspen gproms Excel D. C. Miller, B. Ng, J. C. Eslick, C. Tong and Y. Chen, 2014, Advanced Computational Tools for Optimization and Uncertainty Quantification of Carbon Capture Processes. In Proceedings of the 8th Foundations of Computer Aided Process Design Conference FOCAPD M. R. Eden, J. D. Siirola and G. P. Towler Elsevier. 7

8 Carbon Capture System Superstructure Using Surrogate Models Surrogate models for each reactor and technology used Discrete decisions: Continuous decisions: Unit geometries How many units? Parallel trains? What technology used for each reactor? Operating conditions: Vessel temperature and pressure, flow rates, compositions 8

9 Simultaneous Process Optimization & Heat Integration w/o heat integration Sequential Simultaneous Net power efficiency (%) Net power output (MW e ) Electricity consumption b (MW e ) Base case w/o CCS: 650 MW e, 42.1 % Chen, Y., J. C. Eslick, I. E. Grossmann and D. C. Miller (2015). "Simultaneous Process Optimization and Heat Integration Based on Rigorous Process Simulations." Computers & Chemical Engineering. doi: /j.compchemeng

10 CCSI 2 Industrial Collaboration & Contributions Industrial Collaborations 7 CO 2 Capture Program projects $40MM+ in total project value (TRL 3-7) 6 additional external industrial agreements (executed or in progress) - Cooperative R&D Agreement: GE, ADA-ES, Ion, TCM, SINTEF - Contributed Funds Agreement: COSIA ($500k) Includes enabling capture technology support: - Aerosol, dynamic characterization, turndown, advanced process control Optimal Design of Experiments Improves model while optimizing experimental data generation Applicable to lab through large pilot scale Solvent modeling framework Fundamental characterization of solvent, device and system Collaboration with International Test Center Network (ITCN) & SINTEF 10

11 What was missing in the previous DoE? CCSI using Optimal Design of Experiments to inform test plans Mainly designed using a space-filling approach without considering the output space When designed considering the output space, feedback from the experimental data are not leveraged to update the DOE How to solve these issues? Develop DOE by taking into consideration the output space by using a preliminary process model Use a sequential approach to improve DOE (through improvement of process model, etc.-more on this later) as experimental data are obtained Being employed at NCCC and TCM for Test Campaigns in

12 Absorbe r Typical Steady-State DOE CCSI DOE Approach and Comparison with CCSI Model Regenerator 12

13 Developing Detailed, Predictive Models of Solvent-Based Capture Processes Measurement Uncertainty Pilot/ Commercia l Scale Data Process UQ Steady-State and Dynamic Process Model Measurement Uncertainty Lab Scale Data Measurement Uncertainty WWC/Bench/Pilot Scale Data Properties Package UQ U Q Process Sub-Models Chemistry Model Kinetic model Thermodynami c Models Transport Models Hydrodynami c Models Mass Transfer Models 13

14 Negative Heat of Absorption (kj/mol CCSI Solvent Model is More Predictive Because It Utilizes Better Multiscale Models Developed with a Holistic Approach CO2) Heat of Absorption Data* (30 wt% MEA and 40,80, and 120 C) CO2 Loading (mol CO2/mol MEA) Deterministic Model CO 2 Partial Pressure (kpa) C, 30 wt% MEA 0 CO Loading (mol CO 2 /mol MEA) Stochastic Model (Prior Parameter Distribution) CO 2 Partial Pressure (kpa) C, 30 wt% MEA CO Loading (mol CO 2 /mol Posterior Parameter Distribution Bayesian inference 14

15 Increasing Learning at Pilot Scale with Dynamic Data Collection In conjunction with a dynamic model, a greater amount of data can be collected within the same amount of time during pilot plant testing Demonstrated at NCCC Dynamic data reconciliation enables model validation 6500 Lean Solvent to absorber (kg/h) Time 1.0(h) CO 2 captured (%) Absorber Time 1.0(h) Flue gas flowrate to absorber (kg/h) CO 2 captured (%) Stripper Time (h) Time (h) 15

16 CCSI Toolset Support Advanced Process Control For Carbon Capture Initial Flue Gas Ramp Flue Gas Flow (kmol/hr) Time (sec) Double-Pole Result (New) Fast Response Slow Response 16

17 CCSI Toolset supports oxy-combustion processes & optimization Zone 9 Flue Gas Exit Plane Zone 8 Zone 7 Zone Pollution Controls 5. CO 2 Compression Train Zone 5 Zone 4 Zone 3 Zone 2 Zone 1 Dowling, A. W.; Eason, J. P.; Ma, J.; Miller, D. C.; Biegler, L. T., Equation-Based Design, Integration, and Optimization of Oxycombustion Power Systems. In Alternative Energy Sources and Technologies: Process Design and Operation, Martín, M., Ed. Springer International Publishing: Switzerland, 2016; pp Ma, J.; Eason, J.; Dowling, A. W.; Biegler, L. T.; Miller, D. C., Development of a First-Principles Hybrid Boiler Model for Oxy-Combustion Power Generation System. International Journal of Greenhouse Gas Control 2016, 46,

18 CFD Models Superstructure Optimization Solvent Blend Model Oxycombustion System Optimization Framework CCSI TOOLSET PRODUCTS AVAILABLE AS OPEN SOURCE SUMMER

19 CCSI 2 and Toolset Summary CCSI 2 employs a multi-scale modeling framework (materials through systems) based on fundamental, scientific principles, providing glass-box understanding of capture technology Interconnectivity of scale, physics and chemistry permits well-informed modeling framework with full quantification of uncertainty UQ leveraged to improve model prediction and data generation for lower risk CCS scale up, demo and commercialization High throughput, intelligent computational screening informs most effective R&D pathways Multiple active collaborations with world-class industrial partners and test centers CCSI 2 supports the full commercialization pathway for the following CCS technology platforms: Post combustion solvents, sorbents & membranes; oxycombustion; & compression Open source suite of tools and methodologies can help all capture technology development projects maximize their learning and decrease technical risk 19

20 For more information David C. Miller, Ph.D., Technical Director Michael S. Matuszewski, Associate Technical Director

21 Legend: 5 (Highest capability), 1 (Low capability), - (No capability) Competition Description CCSI Aspen ANSYS Mode Frontier CCSI Advantage Annual cost for a single user within a company. $10k $25k+ $25k+ $10k+ The CCSI Toolset is available for a flat fee of $10k/year, whereas the competition is licensed per concurrent user. Competition cost is Cost Key Features Data Management Process and Device-Scale Models Capabilities of the CCSI Toolset Annual cost for unlimited use within a company. $10k $1,000k+ $1,000k + Specifically designed to support development and scale up of new technology Specifically designed for carbon capture Advanced machine learning tool for determining simple algebraic functional form and parameters of data Applicable to full range of chemical processes Graphical workflows for setting up and analyzing results Data management framework that includes provenance relationships among models and experimental data Simultaneous property and process parameter estimation Property estimation Solvent blends properties estimation tool that minimizes data requirements for new blends Sorbent properties model estimation/fitting tool Dynamic moving bed reactor process model for gas/solid contacting 5 * - - Dynamic bubbling fluidized bed process model for gas/solid contacting 5 * - - Dynamic compression process models tailored for CO 2 5 * - - Membrane models Dynamic solvent models with rate-based mass transfer 5 # - - High resolution "filtered" models for hydrodynamics and heat transfer in a fluidized bed 5 - * - Hierarchically validated device-scale fluidized bed model at multiple scales 4 - * - Oxycombustion boiler model 5 - * - $500k+ estimated based on market research. The CCSI Toolset licensing structure is designed to help companies get the maximum benefits across the entire organization. The CCSI Toolset is the only fully integrated and validated suite of computational tools and models that are specifically designed to support the development and scale up of carbon capture technologies. The CCSI Toolset has a fully integrated data management system to enable provenance tracking (i.e, dependencies) from experimental data to fully optimized process. All solid-gas process models are dynamic, non-isothermal, rigorous, flexible, capable of simulating embedded heat exchangers, and readily configurable for general absorption/ adsorption/ desorption processes. Highly accurate yet computationally efficient process models available in the CCSI Toolset can be readily used for UQ, process control, optimization and scale-up studies. Industry is already teaming with CCSI to utilize the Toolset. #Aspen supports dynamic simulation of solvent-based capture systems, but does directly not support dynamic rate-based simulation. *A user can develop these models using this software, but the software does not have pre-built, validated models available as part of its commercial offerings. Advanced Process Control Nonlinear Model Predictive Control The CCSI Toolset includes the ability to develop fast and accurate nonlinear dynamic reduced models from generic process models (not limited to specific simulation software) and include dynamic uncertainty quantification. Supports advanced process control using computationally efficient algorithms with the flexibility to use opensource nonlinear programming solvers and rigorous disturbance estimation methods. Aspen s nonlinear control algorithms are nongeneric and based on dated and inefficient sub-space identification methods. 21

22 Legend: 5 (Highest capability), 1 (Low capability), - (No capability) Advanced Process Control Competition Description CCSI Aspen ANSYS Mode Frontier Nonlinear Model Predictive Control Multiple Model-based Predictive Control Efficient State/Disturbance Estimation Automated tool for developing fast dynamic, nonlinear models CCSI Advantage The CCSI Toolset includes the ability to develop fast and accurate nonlinear dynamic reduced models from generic process models (not limited to specific simulation software) and include dynamic uncertainty quantification. Supports advanced process control using computationally efficient algorithms with the flexibility to use open-source nonlinear programming solvers and rigorous disturbance estimation methods. Aspen s nonlinear control algorithms are nongeneric and based on dated and inefficient sub-space identification methods. Optimization UQ Workflow Derivative free optimization Process optimization Incorporates code from the world's most advanced algebraic deterministic MINLP optimization solver (BARON) Support for optimization under uncertainty (OUU) Support for external optimization solvers Support for multi-objective optimization Simultaneous property and process parameter estimation with UQ Support for Sensitivity analysis Support for Bayesian statistics to estimate uncertainty and/or parameters Propagation of uncertainty across scales from properties to device to equipment Hierarchical validation framework for CFD models to predict scale up performance with quantitative confidence Integrated multiscale modeling framework that supports models from particle, to device, to process Workflow for developing & validating particle scale models Workflow for developing & validating device scale models Workflow for developing & validating process scale models Ability to automatically manage 1000's of simulations in parallel on cloud-based computers Ability to exploit parallel computing architectures Support for interconnecting multiple software and simulation tools (Aspen, gproms, Thermoflow, Excel, MFIX, Fluent) Automated workflow to enable superstructure optimization using detailed models Automated workflow to enable detailed 2-D and 3-D models to be incorporated into systems models The CCSI Toolset includes the most advanced and flexible set of optimization tools. Aspen s optimization solvers are dated, and modefrontier does not natively link with process simulation software. Only the CCSI Toolset was created from the ground up with uncertainty quantification as a central goal. As such, it is the only tool with integrated Bayesian statistics and uncertainty quantification that goes well beyond simple sensitivity analysis, enabling propagation of uncertainty across scales and enabling Bayesian calibration for unprecedented predictive model capability. Only the CCSI Toolset includes native support for validation using rigorous statistical methods. The CCSI Toolset includes various tools to enable physicsbased scale bridging of multi-physics models across different length scales to allow model-based optimization and uncertainty propagation. 22