2. Project Motivation & Description Accomplished Work Future Work

3.  Coal-fired power plants provide 44.9 % of the electricity consumed in the USA.  847 billion tons of coal reserves worldwide will last around 119 years at current rates of production.  Coal generates 25 % of global greenhouse gas emissions.  CO2 makes up 77 % of global greenhouse gas emissions. http://www.worldcoal.org/coal, http://en.wikipedia.org/wiki/Coal_power_in_the_United_States

4. http://www.geos.ed.ac.uk/sccs/capture/, Integrated Framework for Solvent Selection and Solvent Recycling for CO 2 Capture: August 09 Monthly Report. EPRI, Palo Alto, CA. Product ID # 069040  Carbon Capture Systems: 1.Post-Combustion 2.Pre-Combustion 3.Oxy-Fuel Combustion  Separation Techniques: 1.Physical Absorption 2.Chemical Absorption 3.Adsorption 4.Membrane Separation 5.Cryogenic Separation

5.  Physical and chemical absorption, using amine solvents, for gases with low concentrations of CO2.  CO2 stripping and solvent regeneration.  High energy penalty: 20-40% of plant’s power output Folger, P. (2010). Carbon Capture: A Technology Assessment. Congressional Research Service, (p. 99)

6.  Using different solvents:  Monoethanolamine (MEA)  Diethanolamine (DEA)  Amino Methyl Propanol (AMP)  Solvents with solubility parameters similar to that of CO2  Varying design conditions  Heights of columns  Feed location  Varying operating conditions  Operating temperature  Operating pressure  Solvent flowrate http:// michelledagninosblog.blogspot.com /

7. Use a numerical optimization technique, Simulated Annealing (SA), to minimize the energy consumed by the carbon capture process. Diwekar, U. Introduction to Applied Optimization 2nd Edition. Clarendon Hills: Springer.  Model: Simulation developed in Aspen Plus  Decision Variables: Model Parameters  Objective Function: Energy  Constraints: mass and energy balance, reaction kinetics

8.  Solvent = 30 weight percent MEA solution  Rate-based model NOT Equilibrium Model  Perform Parametric Studies

12.  Possible combinations: Where: 100 5 = 100 samples of each of the 5 continuous variables N a = Maximum number of trays in absorber N s = Maximum number of trays in stripper N a ! = Possible absorber feed tray locations N s ! = Possible stripper feed tray locations  Use simulated annealing, a numerical optimization method, to minimize the energy penalty.

13.  Read “Introduction to Applied Optimization”  Use SA to solve an example problem in Aspen Plus 1.Understand what Simulated Annealing (SA) is 2.Become Familiar with the CAPE-OPEN SA Capability in Aspen Plus

14.  Global optimization technique that:  Mimics physical annealing: Heating and controlled cooling of a material which allows atoms to find configurations with lower internal energy compared to their initial configurations. High Temperature Low Temperature http://on.wikipedia.org/wiki/Simulated-Annealing, Diwekar, U. Introduction to Applied Optimization 2nd Edition. Clarendon Hills: Springer.

15. Goal:  Minimize Objective Function Multiply by (-) to maximize Specify:  Binary variables AND discrete variables Discretize continuous variables  Equality constraints AND inequality constraints  Initial temperature  Freezing temperature  Temperature decrement Simple rule: T new = α T old where 0.8≤ α ≤0.99 Temperature is a parameter

16.  Maintain constant temperature by:  Varying oxygen flow-rate between 5000 and 10000 kmol / hr  Maximizing water flow-rate (-water flow-rate = cost) Oxygen kmol / hrWater kmol / hr OXYGEN FLOW-RATE: 5800 kmol / hr

17.  Continuous Variables  Stripper reflux ratio : optimum at 0.017  LEAN-IN Pressure : optimum at 1.05 atm  RICH-IN Pressure : optimum at 1.07 atm  LEAN-IN Temperature: optimum at 42.28 ⁰ C  Moles CO 2 / Mole MEA: optimum at 0.25  Integer Variables  Stripper feed stage: optimum at stage 6

18. Understood performance of MEA rate based system 1. Parametric studies 2. Simulated annealing  Perform parametric studies and simulated annealing on: 1. DEA system 2. MEA+DEA system 3. New Solvent http://4photos.net/en/image:111-195616-save_energy_pictures_images

19.  National Science Foundation EEC-NSF Grant # 1062943  Dr. Urmila Diwekar  Dr. Juan Salazar  Dr. Christos Takoudis  Dr. Greg Jursich