Multi-Objective Optimization of Solid Sorbentbased CO2 Capture Systems

Transcription

1 Multi-Objective Optimization of Solid Sorbentbased CO2 Capture Systems Miguel Zamarripa, John Eslick, David Miller National Energy Technology Laboratory (NETL) CO 2 Industrial, Engineering and R&D Approaches Session AIChE Annual Meeting, Minneapolis, MN, USA. October 31 st, 2017

2 Importance of Post-combustion Carbon Capture - Schematic Diagram of Thermal Power Plant - US power production in 2015: 2/3 from Fossil fuels. BOILER Fuel Air Steam Flue Gas TURBINE Electricity Post Combustion Capture (PCC) Clean Gas CO 2 S T A C K Compression CO 2 > 99% PURITY STORAGE Flue Gas: Coal Power plant, 650 MW (~27 kmol/s) PCC: Low CO 2 concentration (~12 % vol) Multiple components (H 2 O, N 2, CO, O 2 ) Capture target 90% 2

3 Post-Combustion Carbon Capture Technologies Liquid Solvents absorption Membranes gas permeation Solid Sorbents adsorption Current studies often do not rigorously optimize complete systems considering multiple technology options process configurations operating conditions Goals: Simultaneously optimize the process configuration, process design and operating conditions based on rigorous models. Explore changes in the optimal results (plant design, configuration, and operation) as a function of different capture rates (i.e., 40%, 60%, or 90%) 3

4 Gas Solid contactors (adsorption and regeneration): Bubbling fluidized bed reactors: Solid Sorbent Technologies 1D model (3 regions: Emulsion, Cloud-Wake, Bubble) 1. PDE s + algebraic equations (~14,000 equations). Sorbent properties (Arrhenius constant & activation energy, heat of adsorption). [1] Lee, A., & Miller, D. C. (2012). Industrial & Engineering Chemistry Research,52(1), H D S F Reactor design: Solids Feed (S F, top or bottoms) Overflow and underflow operation Diameter (D), height (H), solid bed depth (L B ) Heat exchanger: # tubes and tube spacing S F Unit level: System level: L B H, D, LB Gas Solid contact Pressure Drop Costs: Operation + Investment 4

5 Superstructure Optimization Framework Clean Gas Solid HX Cooler Solid Gaseous Flue Gas coolant Adsorber Train (beds) No. parallel trains Flue Gas HX A A A a n a 2 a 1 B B B Solid HX A A A d n d 2 d 1 B B B CO 2 & H 2 O Hot in Regeneration Train (beds) Steam gas to storage MINLP Problem Complexity Increases with: - # of technologies - # of stages - Non-linearities of the problem Discrete Decisions: Continuous decisions: No. of Parallel trains? How many beds (Ads and Rgn)? What technology used for each reactor (A or B)? Unit Dimensions (D, h, HX area) Operating conditions (T, P, F, z) Fixed & Operating Cost 5

6 Cost of Electricity min COE = Investment + Operating fix + Operating var Net Power Costing Methodology: Investment cost Sorbent, Power Plant, Capture (ads, rgn, HX, cmp). Operating cost: Fixed: labor, maintenance, others. Variable: utilities coolant & steam, waste water, others. Net power: Power PP (kw for compression, blowers, pumps, etc). s. t. Material Balances Energy Balances Equipment Design Process Configuration Capture Target Quality Guidelines for Energy System Studies: Performing a Techno-economic Analysis for Power Generation Plants (DOE/NETL-2015/1726) Capital cost levels and their elements Product and Process Design Principles Synthesis (Seider et al., 2009) Purchase cost calculations 6

7 Solid In Gas Out Gas In Solid Out Process Models Utility Out Utility In Multi-objective Analysis Surrogate Models (nonlinear models suitable for optimization) + First Principle Models Superstructure Opt. Model Surrogate Model Generation and Validation 7

8 Solid In Gas Out Gas In Solid Out Process Models Utility Out Utility In Multi-objective Analysis Surrogate Models (nonlinear models suitable for optimization) + First Principle Models Superstructure Opt. Model BFB for Adsorption & Regeneration Detailed ACM simulation. SolidIn {Fm, S o lid _ P, In T, w(bic), w(car), w(h 2 O)} HXIn {F, T} Reactor Design Dt unit diameter Heat Exchanger design Solids bed depth H X _ In G a s_ O u t B FB A D S G a s_ In GasOut {F, P, T, z("co 2 "), z("h 2 O"), z("n 2 ")} S o lid _ O u t H X _ O u t HXOut {F, T} SolidOut {Fm, P, T, w(bic), w(car), w(h 2 O)} 17 inputs vars GasIn {F, P, T, 12 outputs vars z("co 2 "),z("h 2 O"), z("n2")} ~14,000 equations 12 EQUATIONS 7

9 Surrogate Gas Outlet Flow rate Surrogate Gas Outlet Flow rate Solid In Gas Out Gas In Solid Out Process Models Utility Out Utility In Multi-objective Analysis Surrogate Models (nonlinear models suitable for optimization) + First Principle Models Superstructure Opt. Model BFB for Adsorption & Regeneration Detailed ACM simulation. Data Management Run ALAMO Validation Data Set: 2000 samples Latin Hypercube Sampling method Fit data R 2 = 0.99 Rigorous Gas Outlet Flow rate Cross-Validation 200 samples LHS method R 2 = 0.99 Rigorous Gas Outlet Flow rate 7

10 Solid Sorbent System Case Study Adsorption system Clean Gas SolidLeanHX Rich CO 2 Gas to storage Plant consists of: Flue gas (650 MW power plant) 90 % capture needed CO 2 ~12% (molar fraction) 4 adsorber & regeneration beds 2 technologies (reactor configuration) Adsorber beds 4 12 parallel units. # Nu 4-12 Mathematical Model First principle Surrogate models. Flue Gas FG_HX SolidRichHX Gas Regeneration beds 8

11 Optimal Solutions Summary: Superstructure optimization allow us to explore all the possible plant layouts. Optimization problem (GAMS/Dicopt): 383 equations 588 variables (24 Discrete) 90% CO 2 Capture. Different initialization Fixed layout Optimal Case 1 Case 2 Case 4 Case 5 Case 6 Case 7 % COE increase Adsorber beds Regeneration beds Ads parallel units Rgn parallel units

12 Relative COE (%, $/MWh) Cost of electricity due to capture Capture target (90% - Base Case) COE vs Capture Target Parallel Trains Cap 40 Cap 60 Cap Capture Target NuAd NuRg 10

13 Millions Relative COE ($/MWh) Cost of electricity due to capture Capture target (90% - Base Case) COE vs Capture Target kg steam / tco2 (% increase) Unit design cost (%) 0 40% 60% 90% Capture Target % 60% 90% Adsorption Cost Regeneration Cost Solid flowrate (kg/hr) Cap 40 Cap 60 Cap 90 10

14 Remarks Superstructure optimization is challenging PDE models replaced by surrogates Integrated conceptual design and process synthesis tools Facilitate rapid development Robust mathematical optimization framework Optimal process configuration changes with capture target Demonstrates importance of conceptual design Complements typical flowsheet optimization Potential extension for multiple technologies 11

15 Acknowledgments National Energy Technology Laboratory and Oak Ridge Institute for Science and Education (ORISE). Thank you for your attention Disclaimer 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.

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