Major: Interdisciplinary program of the integrated biotechnology

1. 다중규모 모사Multiscale simulation for process development[Example 3: Multiscale modeling in product engineering] Major: Interdisciplinary program of the integrated biotechnology Graduate school of bio- & information technology Youngil Lim (N110), Lab. FACS phone: +82 31 670 5207 (direct) Fax: +82 31 670 5445, mobile phone: +82 10 7665 5207 Email: limyi@hknu.ac.kr, homepage:http://facs.maru.net 2 weeks topic

2. Product engineeringProcess engineering

3. To increase predictability of modelsTo speed up the process design procedureTo decrease costs for process development Motivation of multi-scale simulation (MSS)

4. Example 3. Multiscale modeling in product engineering • + PSM (process systems modeling): process design, operation, optimization, control, intensification, and flowsheet • Process-level simulation (PLS) by Lim • + CFD (computational fluid dynamics): to solve continuity equation, Navier-Stokes equation, mass and energy transport for fluid hydrodynamics • Fluid-level simulation (FLS) by Lim • + CCH (computational chemistry): to predict the molecule geometry and its chemical properties, and thermodynamic macro-properties of pure species or mixtures. • Molecular-level simulation (MLS) by Lim What is problem on this classification? Jaworski and Zakrzewska(2011), Toward multiscale modeling in product engineering, Computers and chemical engineering 35, 434-445.

5. Example 3. Multiscale modeling in product engineering Process simulation using a process simulator (e.g., ASPEN Plus, or Hysys) Werther et al. (2011), The ultimate goal of modeling—Simulation of system and plant performance. Particuology 9, 320-329.

6. Example 3. Multiscale modeling in product engineering MSS approach for deep understanding Werther et al. (2011), The ultimate goal of modeling—Simulation of system and plant performance. Particuology 9, 320-329.

7. Example 3. Multiscale modeling in product engineering Werther et al. (2011), The ultimate goal of modeling—Simulation of system and plant performance. Particuology 9, 320-329.

8. Example 3. Multiscale modeling in product engineering Werther et al. (2011), The ultimate goal of modeling—Simulation of system and plant performance. Particuology 9, 320-329.

9. Example 3. Multiscale modeling in product engineering • + Linkage between PLS and FLS? • + Linkage between FLS and MLS? • Multi-scale nature in the real world • + Problem (chemical engineering problems) • + Model (equations describing physical phenomena) • + Software (commercial or home-made programs) • + Hardware (PC, workstation, super computers or parallel networking computing) • Meso-scale simulation is a bottleneck. • EMMS (energy-minimized multiscale approach) • Compromise between complexity (micro-scale) and diversity (macro-scale) Jinghai Li et al.(2011), Real time simulation of chemical processes: Dream or reality, ECCE2011, Keynote lecture.

10. Example 3. Multiscale modeling in product engineering: PLS + PLS (process-level simulation) - steady-state modeling: mass and energy balance at steady-state, equilibrium equations e.g., thermodynamic properties, equation of state (EOS)  algebraic equations, g(x,u) = 0 - unsteady-state modeling: time evolution models, mass and energy balance at unsteady-state, transport equations e.g., momentum, mass and energy transport equations, chemical reaction rate  differential equations, g(dx/dt, du/dt, x, u, t) = 0 - optimization modeling: objective function and constraints e.g., Minimize the cost with constraints of mass and energy balances  - simulation tools: modular-oriented strategy and equation-oriented strategy e.g. ASPEN Plus, Hysis, Prosim Plus, ASPEN dynamics, … gProms, Matlab Jaworski and Zakrzewska(2011), Toward multiscale modeling in product engineering, Computers and chemical engineering 35, 434-445.

11. Example 3. Multiscale modeling in product engineering: FLS + FLS (fluid-level simulation) - continuous fluid phase is considered, represented by partial differential equation (PDE) for momentum, mass, and energy transport  PDE with accumulation, convection, diffusion and source terms - simulation tools: FVM (finite volume method), FEM (finite element method)  ANSYS Fluent (FVM): strong geometry construction tool, many examples ComSol Multiphysics (FEM): Matlab-based CFD, equation-oriented approach - hybrid CFD tools:  CFD + LBM (lattice-Boltzmann method): CFD for fluid, LBM for micro-channel mass transfer. CFD + DEM (discrete element method): CFD for fluid, DEM for moving particles (Werther et al) CFD + PIC (particle-in-cell): CFD for fluid, PIC for particle probability distribution function, CPFD (computational particle fluid dynamics) called Barracuda (Snider et al., 2011). Jaworski and Zakrzewska(2011), Toward multiscale modeling in product engineering, Computers and chemical engineering 35, 434-445. Werther et al. (2011), The ultimate goal of modeling—Simulation of system and plant performance. Particuology 9, 320-329. Snider et al. (2011), Eulerian-Lagrangian method for 3D thermal reacting flow with application to coal gasifier, Chem. Eng. Sci., 66, 1285-1295.

12. Example 3. Multiscale modeling in product engineering: MLS + MLS (molecular-level simulation) - discrete phase (or molecules) is considered to predict the molecule position, chemical properties, and thermodynamic macroscopic properties of pure species or mixture.  MC (Monte-Carlo) modeling for equilibrium property: GCMC (grand canonical MC), MD (molecular dynamics) modeling for diffusion and viscosity flow: EMD (equilibrium MD), NEMD (non-equilibrium MD) - Newton’s dynamics law for a molecule of mass, m - Lennard-Jones energy potential for a molecule: - GCMC (grand canonical Monte Carlo) which uses -V-T ensemble (chemical potential, volume and T are constant), Snurr et al. (1993). - EMD: using NVT ensemble and no apparent flux (no driving force within a given system), calculate mean displace distance caused by self and collective diffusion during a given time, Arya et al. (2001). - simulation tools:  Material Studio (Accelys, USA): Multiscale simulation tool for DFT (density functional theory), MC, MD, DPD (dissipative particle dynamics). Jaworski and Zakrzewska(2011), Toward multiscale modeling in product engineering, Computers and chemical engineering 35, 434-445. Snurr et al., (1993), Prediction of adsorption of aromatic hydrocarbons in silicalite from grand canonical Monte Carlo simulations with biased insertions, J. Phys. Chem. 97, 13742–13752. Arya et al. (2001), A critical comparison of equilibrium, nonequilibrium and boundary-driven molecular dynamics techniques for studying transport in microporous materials, J. Chem. Phys. 115, 8112–8124.

13. Example 3. Multiscale modeling in product engineering: PLS+FLS + PLS + FLS - crystallization process + PLS + MLS or FLS + MLS Jaworski and Zakrzewska(2011), Toward multiscale modeling in product engineering, Computers and chemical engineering 35, 434-445.

14. Summary of Example 3 • Multi-scale simulation approach • Advantages • Limitations and Challenges