Dynamic modeling of CO 2 absorption plants for post combustion capture

Transcription

1 1st Post Combustion Capture Conference (PCCC1) 17th -19th May 2011, Abu Dhabi Dynamic modeling of CO 2 absorption plants for post combustion capture Development, validation and example case study Andrew Tobiesen a * Magne Hillestad b, Hanne M. Kvamsdal a, Actor Chikukwa a a SINTEF Materials and Chemistry, Postbox 4760 Sluppen,7494, Trondheim, Norway b Norwegian University of Science and Technology, NTNU, Sem Sælandsvei 4, Trondheim, 7491, Norway NTNU 1

2 Rationale The objective of this work is to develop and implement dynamic models in CO2SIM in order to simulate a closed loop transient CO 2 absorption system Focus is placed on competence building through use of simulation models that can be: verified against experiment and plant data where feedback to the work is done fairly quickly by developing dynamic simulation models based on already established computer architecture In general, to assist in understanding CO 2 capture processes (or any chemical process), we believe process simulation is beneficial to all phases in a projects lifetime NTNU 2

3 Reasons for developing a dynamic simulator We are now moving towards building full scale plants Large and frequent load changes of power plant requires understanding of dynamic operation How shall the capture plant be operated and controlled? What are the real consequences of varying loads at the CO 2 removal plant? How do we handle unsteady behavior, shut down, start up? Pressurized NG Exhaust: CO 2,N 2, O 2,H 2 O HRSG Amine absorption 40 C N 2, O 2, H 2 O 45 C Condenser 1.9 bar CO 2 to compression Amine stripper ST CO 2 -rich 30 wt % MEA/H2O Re-boiler Air GT Generator LP steam(3.8 bar, 140 C) CO 2 -lean 30 wt % MEA/H2O NTNU 3

4 Difficult to foresee dynamic behavior of complex chemical processes Especially integrated processes such as that of a CO 2 capture plant downstream from the power plant A dynamic simulator will help us understanding such process dynamics NTNU 4

5 Dynamic simulation of the capture plant Existing tools like UniSimDesign and Aspen Hysys Have dynamic simulation options Columns are modeled as equilibrium stages No in-depth control over models and assumptions (black box) CO2SIM A general purpose steady-state flow sheet simulator particularly developed for CO 2 absorption processes Have been developing this software for several years and includes advanced objectoriented programming techniques for efficient and fast development Reuse of class methods Existing thermodynamic and hydraulic models as well as solvers are re-used Existing use of simulator architecture with GUI and visualization methods Exact understanding as to how the models are developed, w.r.t. assumptions: mathematics, physics, algorithms/numerics Research organization: We develop and test own solvents and it is therefore necessary to have insight to our modeling methods, at all layers of the code NTNU 5

6 What dynamic simulator features we would like to have Sub models describing thermodynamics and mass transfer with sufficient detail to yield predictive power (rate based, etc) Implement solvent systems based on established routines from CO2SIM (kinetics, VLE etc) Ability to visualize results General plotting functionality All state variables as well as derived variables should be made available in the integration time horizon Efficient validation of models by use of data standardized extraction routines Streamlined information flow from pilot plant to simulation Batch testing of all available campaign data Robust numerics when using the simulator (we want to converge the system..) Ability to handle significant changes in load conditions (use of stiff DAE solver) Be able to execute in real time to give a virtual response close to the actual system NTNU 6

7 Development Strategy: Task 1: Development of the dynamic CO2SIM column model Development of the model for absorption and desorption Test model and validate towards pilot plant data (steady state and dynamic) Task 2: Implementation of the connected unit operations, flash tanks, mixers, storage tanks and heat exchangers Definition of unit operations built into CO2SIM Task3: Implementation of the dynamic Network solver Flow sheet model Programming techniques: information handling Information structure relationship between an event (the cause) and a second event ( the effect) ->causality NTNU 7

8 Task 1: The transient column model Based on first principle conservation laws for energy and mass Adaptability of the code for different chemical systems and process configurations has been emphasized Uses CO2SIM architecture The subprograms and unit operations within the main module are developed using standardized syntax Handling of events Data extraction routine from pilot plant to simulator NTNU 8

9 Outline Dynamic modeling of CO2 absorption plants Simulator requirements/capabilities Short description of simulation model Verification/ (Preliminary) Experimental validation using pilot plant data Ramp behavior Robustness of code Further work Acknowledgements NTNU 9

10 Experimental validation of dynamic column model Absorber: Packing height = 5.4m ID = 0.5m Tested towards the VOCC rig Two time series cases (case A and B) 30wt%MEA Logging of input and output data every 5 seconds The cases give about 500 updates during simulation CO2SIM handles all these events automatically during integration to reflect process changes. The events are collected and systematically handled from log files (excel). of CO2 Capture - TheVOCC-project (2007) NTNU 10

11 VOCC test case Case B: Stepwise variations in CO 2 gas concentration Inlet gas concentration of CO 2 increased in two single step-changes then decreased in a large reverse single step-change All other process variables kept constant Stable liquid and gas flow over the experiment. Property: molfracco2vap S ummary Property logging of inputs (only at events): molfracco2vap and phase:vap NTNU 11

12 VOCC Cas e B Property logging of outputs (only at events): temp and phase:liq 0.05 S ummary Property logging of inputs (only at events): molfracco2vap and phase:vap Property: temp Blue line SIMULATION Red line - PILOT Simulated and measured: liquid solvent outlet temperature Property: molfracco2vap Input data (Pilot and Simulation) Input co2 concentration (molfraction) NTNU 12

13 0.42 VOCC Case B Property logging of outputs (only at events): loading and phase:liq S ummary Property logging of inputs (only at events): molfracco2vap and phase:vap Property: loading Blue line SIMULATION Red line - PILOT Property: molfracco2vap Input data (Pilot and Simulation) Simulated and measured: Rich loading Input CO2 concentration (molar fraction) NTNU 13

14 Simulation examples Case A: Increasing gas load Increasing molar ratio between the gas and liquid flow rate Varying the liquid and vapor input flow rates by ~50%? In a time frame of 50 seconds Initially running at steady state then increase gas flow with 50% over a short time interval Observe the transients, then run end situation to steady state again Property: flow Property logging of inputs (only at events): flow and phase:vap 20 meter packing, identical inlet concentrations, only vary flow rate Blue: gas flow rate (kmol/h) Green: liquid flow rate (kmol/h) NTNU 14

15 Case A: Increasing the flue gas load Effect on loading Property logging of outputs (only at events): loading and phase:liq 0.43 Property: loading Property logging of inputs (only at events): flow and phase:vap 130 Property: flow Green liquid flow rate (kmol/h) Blue: gas flow rate (kmol/h) NTNU 15

16 Case A: Increasing the flue gas load Effect on percent CO2 removed 0.85 Property logging of inputs (only at events): gasremoved and phase:vap Property: gasremoved Property logging of inputs (only at events): flow and phase:vap Property: flow Green liquid flow rate (kmol/h) Blue: gas flow rate (kmol/h) NTNU 16

17 Case B: Varying flue gas load Varying the molar ratio between the gas and liquid flow rate In a time frame of ~300 seconds Initially running at steady state then increase/reduce gas flow with 50% Observe the transients, then run end situation to steady state again 20 meter packing, identical inlet concentrations, only vary flow rate Property: flow Property logging of inputs (only at events): flow and phase:vap Blue: gas flow rate (kmol/h) Green: liquid flow rate (kmol/h) NTNU 17

18 Case B: Varying the flue gas load Effect on loading Property logging of outputs (only at events): loading and phase:liq 0.42 Property: loading Property logging of inputs (only at events): flow and phase:vap 130 Property: flow Green liquid flow rate (kmol/h) Blue: gas flow rate (kmol/h) NTNU 18

19 Case B: Varying the flue gas load Effect on CO2 removed Property logging of inputs (only at events): gasremoved and phase:vap Property: gasremoved Property logging of inputs (only at events): flow and phase:vap Property: flow Green liquid flow rate (kmol/h) Blue: gas flow rate (kmol/h) NTNU 19

20 At this point in the project a robust codebase is developed for dynamic simulation Absorber, desorber packing model (this presentation) Verified numerics Validated towards plant data (preliminary) The event updating procedures facilitates rapid simulation using plant data for validation Current model gives acceptable match towards pilot plant data for MEA Both at dynamic and steady state operation The implementation methodology allows for efficient simulation of the units transient behavior for continuously changing input conditions or design parameters, part load operation, varying input conditions and ramping behavior. NTNU 20

21 Currently under development Network solver to handle sequential dynamic integration (finished but needs testing) Builds the network from a GUI (graphical user interface) Including recycles Integrated handling of events (input changes) during simulation of networks with recycles A few units implemented: Storage tank, dynamic flash, mixer tank and the column model NTNU 21

22 Acknowledgement This presentation forms a part of the BIGCO2 project, performed under the strategic Norwegian research program Climit. The authors acknowledge the partners: StatoilHydro, GE Global Research, Statkraft, Aker Clean Carbon, Shell, TOTAL, ConocoPhillips, ALSTOM, the Research Council of Norway (178004/I30 and /I30) and Gassnova (182070) for their support. NTNU 22

23 Example simulation case study Changing the power plant load: increasing/decreasing the volumetric ratio between gas flow rate and liquid flow rate What are the consequences of varying the liquid and vapor input flow rates by ~50%? Running at steady state, vary loads at a short time interval to see the transients, then run end situation to steady state again 20 meter packing, identical inlet concentrations, only vary flow rate Property: flow Property logging of inputs (only at events): flow and phase:vap red = liquid flow blue = gas flow x 10 4 NTNU 23

24 Simulation Case study Property logging of outputs (only at events): temp and phase:vap S ummary Property logging of outputs (only at events): temp and phase:liq Property: temp Property: temp x 10 4 x 10 4 Simulated outlet vapor and liquid temperatures at each event NTNU 24

25 Property: loading Simulation Case study Property logging of outputs (only at events): loading and phase:liq Property: gasremoved S ummary Property logging of inputs (only at events): gasremoved and phase:vap x 10 4 Simulated rich loadings and percent CO2 removed from gas at each event x 10 4 NTNU 25

26 Simulation Case study Property logging of outputs (only at events): loading and phase:liq S ummary Property logging of inputs (only at events): gasremoved and phase:vap Property: loading Property: gasremoved x 10 4 Simulated outlet vapor and liquid temperatures, forward to steady state at each event x 10 4 NTNU 26

27 Case B: Varying the flue gas load Effect on outlet solvent temperature Property logging of outputs (only at events): temp and phase:liq Property: temp Property logging of inputs (only at events): flow and phase:vap Property: flow Green liquid flow rate (kmol/h) Blue: gas flow rate (kmol/h) NTNU 27