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Computational Approach to Quantify Condenser Operations

Computational Approach to Quantify Condenser Operations. AIChE Annual Meeting, 31 st October 2005 Cincinnati, OH #134 - Retrofit Design for Better Economic and Environmental Performance Romeo Ibrahim*, Michalis Xenos, Andres Malcolm, Linas Mockus**, Andreas A. Linninger

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Computational Approach to Quantify Condenser Operations

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  1. Computational Approach to Quantify Condenser Operations AIChE Annual Meeting, 31st October 2005 Cincinnati, OH #134 - Retrofit Design for Better Economic and Environmental Performance Romeo Ibrahim*, Michalis Xenos, Andres Malcolm, Linas Mockus**, Andreas A. Linninger Laboratory for Product and Process Design, Departments of Chemical and Bio-Engineering, University of Illinois, Chicago, IL, 60607, U.S.A. *Abbott ** Pharmacia

  2. Motivational Slide – Multiphase Flow Multiphase Flow: The central theme of this work aims at studying the dynamics of multiphase flow in a condenser. Gas Outlet to Atmosphere Charge Solvent VOC Stream Cryogenic Unit Reactor Condenser Reactor Cooling Water Condensate Liquid Solvent Purpose: Existing models: Lamped which does not evaluate specific controlled volumes. Environmental: Reduce VOCs emission. Simulation: Model optimal conditions and retrofitting with respect to condensation. Flow region: Predict mass and heat transfer. Phases Involved: Bulk Gasand Liquid

  3. Goals - Objectives • Optimize and retrofit condensation operations of VOCs recovery. • Understand principles of transport problems in multiphases flow. • Study the transport phenomena using specialized numerical techniques (Large Scale Computations). • Modeled 2D distributed system (not lamped). • Determined mass transfer of a thin film condensed on the cooler interface.

  4. Concept of designing a model for a condenser • Coupling heat and mass transfer along with the film condensation equations and simultaneously solve numerically for a condenser in a -steady state and - dynamic mode • Understand the approach for solving nonlinear algebraic equations • Results and discussion • Validation • Conclusion Overview

  5. Adiabatic - Insulated Wall Bulk flow Plate Heat exchanger Condenser 350 °K Hot gas Condensable VOC Coolant 293 K Development of 2D Model Mass and Heat Transfer • Simplify 2D geometry one tube real dimensions and properties. • Two parallel plates. • Heat flow through the tube. • Heat exchanged and thin film deposition on the bottom cooler wall. Significant: Mass transfer on the lower wall. Tube

  6. Equations (Steady State)-Cartesian Coordinate The system will first solve for continuity, x and y momentum equations. After obtaining the flow fields, I solve for concentration and energy equations. Continuity X-momentum Y-momentum Temperature - Heat Convection and Conduction Concentration A system consists of 5 Eqs for 5 unknowns (u, v, p, T, C)

  7. B1 Boundary Conditions VOC CONDENSATION IN A THIN FILM B2 Plug Flow B1 B2 B3 B5, 6 B4 W film condensation B5 B3 B4 B6 y Coolant below condensable wall L length L x Parabolic Flow width W No slip condition So u =v = 0 • Energy lost through conduction is almost zero. • Fully established flow field

  8. Adiabatic wall Condenser Ccell T Tcell P g C Condenser ,Gas Mixture ,Gas Mixture Gas Gas ,Condensable ,Condensable Formation of the thin film Gas Boundary Layer Gas Boundary Layer C P P T ∞ S Condensate Film Condensate Film T∞ I C (T (T I I ) ) T T Coolant Wall wall coolant Function of Reynolds and Prandtl numbers Thin Film Condensation The mass fraction of condensable species in the vapor at the gas-condensate interface. The Antoine equation is used to calculate the saturation vapor pressure. Thin film condensation is modeled as a source term near the bottom wall

  9. Discretization of Transport Equations Finite volume (FV) approach was used and here is a visualization for 1D case: Example: Finite Volume for One Dimensional Heat Conduction Δx W e w P E δxw δxe Control volume- 1D We balance the fluxes across the control volume Discretized Transport Equations S is the heat generation per unit volume

  10. Discretization of Transport Equations N n w e W E P s S Converts PDE into system of nonlinear algebraic equations Residual equations F(x) = 0 • Discretized transport equations: • Continuity • x,y-Momentum • Species Transport + Condensation • Energy Equation Continuity X-momentum Y-momentum Simultaneous numerical solution: Newton Raphson Cubic line-search Globally convergence Staggered Grid Approach: Avoids spurious solution Staggered Grid Approach – 3 overlapping Grids

  11. Results and Discussion – Steady State uinflow= 0.5 m/s uinflow= 0.1 m/s Convective Diffusive Velocity magnitude Concentration Temperature 309 K 293 K

  12. Results and Discussion – Steady State 1 m/s VOC f low rate 1 m/s 0.5 m/s Temperature 0.5 m/s 0.1m/s 0.1m/s Concentration in Length 0.1m/s Flux concentration Temperature Flux Heat and mass transfer are more effective when velocity is high

  13. Equations (Dynamics)-Cartesian Coordinate The system will first solve for continuity, x and y momentum equations. After obtaining the flow fields, I solve for concentration and energy equations. Continuity X-momentum Y-momentum Temperature - Heat Convection and Conduction Concentration • System consists of 5 Eqs for 5 unknowns (u, v, p, T, C) • After FV Discretization: => F(x)=0 • Time integration with Implicit Euler, Newton Raphson for • simultaneous solution of all transport eqs.

  14. Results and Discussion – Dynamic Work Applying a step change in temperature from 350 K to 380 K for benzene Step from 350K-380K Mean concentration Temperature Mass Fraction Mean temperature Dynamically heat transfer and concentration profiles can be determined

  15. Validate Thin Film Model • Trial version of Fluent vs. my model. • Validation of the mass transfer: integral balances over the domain. • The code is grid independent, The results are stable when sufficiently fine grid is chosen:

  16. Fluent Developed Model Developed Model Fluent Temperature and velocity profiles at the outflow Validation - Comparing the Flow Field Observe the temperature and velocity profiles are similar in the outflow However, Fluent doesn't solve mass and transfer thin film condensation

  17. 3-D Preliminary Results Using Commercial Simulator 3-D Grid Created For Computational Fluid Dynamics Simulator - - - • Velocity Of Fluid Flowing In Heat Exchanger • Velocity increases around baffles • Baffles create larger heat exchange area • Temperature Of Fluid In Heat Exchanger • Energy equation coupled with velocity • Realistic temperature profile

  18. Temperature Dynamic in 3D Heat Exchanger

  19. Conclusions • Successfully use Navier Stokes equation using heat and mass transfer in 2D. • Using Finite volume to scale none-linear algebriac equations • Validate results against know commercial tools. • Predict optimal condenser operations - reduction of emitted VOCs by appropriate conditions. - increase the efficiency of the condenser. - design condenser for optimum range of pollutant condensation. • Economical incentives - lower the costs (i.e. energy, manpower, and raw materials) - avoid the risk of shutdown - recycle the condensate and sell

  20. Questions

  21. EPA Regulations – Pollution Prevention EPA regulates the amount of VOC for each industry. If the EPA requirements are exceeded then the EPA can close down the industry. • Clean Air Act 1990 • EPA regulations apply if VOC emissions exceed 15 lbs/day • Treatment options • Condensation Source: EPA Office of Compliance Sector Notebook Project: Profile of the Pharmaceutical Manufacturing Industry; September 1997

  22. 0.5 m/s 0.01m/s 0.1m/s 1 m/s Results and Discussion – Steady State Influence of velocity on temperature outflow Increase in velocity caused increase in temperature

  23. Coolant Inlet Baffles Vapor Inlet Coolant Outlet Coolant is circulated around the cap Vapor Outlet Condenser Design

  24. Results and Discussion – Steady State Variation of velocity with respect to length 1 m/s Mass Transfer Increases 0.5 m/s Heat Transfer Increases 1 m/s Temperature Concentration 0.5 m/s 0.1m/s 0.1m/s Heat and mass transfer are faster when velocity is high

  25. Comparison of MOL, LMTD, FLUENT 405 405 395 395 385 385 375 375 365 365 Temperature Temperature 355 355 345 345 335 335 325 325 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 Length Length LMTD Method LMTD Method MOL Model MOL Model FLUENT Simulation FLUENT Simulation Comparison of Methods • LMTD Model: • most commonly used • only steady state • MOL: • numerical • dynamic • Fluent: Computation Fluid Dynamic Simulator

  26. Results and Discussion – Steady State Variation of velocity with respect to length 1 m/s Mass Transfer Increases 0.5 m/s Heat Transfer Increases 1 m/s Temperature Concentration 0.5 m/s 0.1m/s 0.1m/s Heat and mass transfer are faster when velocity is high

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