Multiscale systems engineering with applications to chemical reaction processes

Chemical Engineering Science

Volumn 59, Issue 22-23, 2004, Pages 5623-5628

  Braatz, Richard D ^a   Alkire, Richard C ^a   Rusli, Effendi ^a   Drews, Timothy O ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Multiscale system; Stochastic optimization; Stochastic simulation

Indexed keywords

MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; SENSITIVITY ANALYSIS;

MULTISCALE SYSTEMS ENGINEERING; NANOSCOPIC SCALES;

REACTION KINETICS;

CHEMICAL ENGINEERING;

EID: 10044222036     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2004.09.022     Document Type: Conference Paper

References (30)

1. Aarts, E.H.L., Korst, J.H.M., 1989. Simulated Annealing and Boltzmann Machines: a Stochastic Approach to Combinatorial Optimization and Neural Computing, Wiley, New York.
2. Alkire, R., Verhoff, M., 1998. The bridge from nanoscale phenomena to macroscale processes Electrochimica Acta 43, 2733-2741.
3. Braatz, R.D., 2002. Advanced control of crystallization processes. Annual Reviews in Control 26, 87-99.
4. Drews, T.O., Braatz, R.D., Alkire, R.C., 2003a. Parameter sensitivity analysis of Monte Carlo simulations of copper electrodeposition with multiple additives. Journal of the Electrochemical Society 150, C807-C812.
5. Drews, T.O., Xue, F., Li, X., Deligianni, H., Vereecken, P., Cooper, E., Andricacos, P.; Braatz, R.D., Alkire, R.C., 2003b. Parameter estimation of a copper electrodeposition additive mechanism using data obtained from a D-optimal experimental design. In: Proceedings of the Topical Conference on Electrodeposition Processes, Paper 189b, A.I.Ch.E. Annual Meeting, San Francisco.
6. Drews, T.O., Webb, E.G., Ma, D.L., Alameda, J., Braatz, R.D., Alkire, R.C., 2004. Coupled mesoscale-continuum simulations of copper electrodeposition in a trench. A.I.Ch.E. Journal 50, 226-240.
7. Drexler, K.E., 1992. Nanosystems: Molecular Machinery, Manufacturing, and Computation, Wiley Interscience, New York.
8. Feehery, W.F., Tolsma, J.E., Barton, P.I., 1997. Efficient sensitivity analysis of large-scale differential-algebraic equations. Applied Numerical Mathematics 25, 41-54.
9. Fichthom, K.A., Weinberg, W.H., 1991. Theoretical foundations of dynamical Monte Carlo simulations. Journal of Chemical Physics 95, 1090-1096.
10. Gallivan, M.A., Goodwin, D.G., Murray, R.M., 2001. Modeling and control of thin film morphology using unsteady processing parameters: problem formulation and initial results. In: Proceedings of the 40th IEEE Conference on Decision and Control. IEEE Press, Piscataway, NJ.
11. Gunawan, R., Jung, M.Y.L., Seebauer, E.G., Braatz, R.D., 2003. Maximum a posteriori estimation of transient enhanced diffusion energetics. A.I.Ch.E. Journal 49, 2114-2123.
12. Gunawan, R., Jung, M.Y.L., Seebauer, E.G., Braatz, R.D., 2004. Optimal control of transient enhanced diffusion in a semiconductor process. Journal of Process Control 14, 423-430.
13. Hoummady, M., Fujita, H., 1999. Micromachines for nanoscale science and technology. Nanotechnology 10, 29-33.
14. Jockenhovel, T., Biegler, L.T., Wachter, A., 2003. Dynamic optimization of the Tennessee Eastman process using the OptControlCentre. Computers and Chemical Engineering 27, 1513-1531.
15. Katsoulaokis, M.A., Majda, A.J., Vlachos, D.G., 2003. Course-grained stochastic processes and Monte Carlo simulations in lattice systems. Journal of Computational Physics 186, 250-278.
16. Lee, S.Y., Lee, S.J., Jung, H.T., 2003. Protein microarrays and chips. Journal of Industrial Engineering Chemistry 9, 9-15.
17. Li, S., Petzold, L.R., Zhu, W., 2000. Sensitivity analysis of differential-algebraic equations: a comparison of methods on a special problem. Applied Numerical Mathematics 32, 161-174.
18. Lou, Y.M., Christofides, P.D., 2003. Estimation and control of surface roughness in thin film growth using kinetic Monte-Carlo models. Chemical Engineering Science 58, 3115-3129.
19. Makeev, A.G., Maroudas, D., Kevrekidis, I.G., 2002. Course stability and bifurcation analysis using stochastic simulators: kinetic Monte Carlo examples. Journal of Chemical Physics 116, 10083-10091.
20. Matthews, H.B., Rawlings, J.B., 1998. Batch crystallization of a photochemical: modeling, control and filtration. A.I.Ch.E. Journal 44, 1119-1127.
21. Nagy, Z.K., Braatz, R.D., 2003a. Robust nonlinear model predictive control of batch processes. A.I.Ch.E. Journal 49, 1776-1786.
22. Nagy, Z.K., Braatz, R.D., 2003b. Worst-case and distributional robustness analysis of finite-time control trajectories for nonlinear distributed parameter systems. IEEE Transactions on Control Systems Technology 11, 494-504.
23. Pan, W.W., Tatang, M.A., McRae, G.J., Prinn, R.G., 1998. Uncertainty analysis of indirect radiative forcing by anthropogenic sulfate aerosols. Journal of Geophysical Research-Atmospheres 103, 3815-3823.
24. Prokop, A., 2001. Bioartificial organs in the twenty-first century - Nanobiological devices. Bioartificial organs III: tissue sourcing, immunoisolation, and clinical trials. Annals of the New York Academy of Sciences 944, 472-490.
25. Raimondeau, S., Vlachos, D.G., 2002. Recent developments on multiscale, hierarchical modeling of chemical reactors. Chemical Engineering Journal 90, 3-23.
26. Raimondeau, S., Aghalayam, P., Mhadeshwar, A.B., Vlachos, D.G., 2003 Parameter optimization of molecular models: application to surface kinetics. Industrial Engineering Chemistry Research 42, 1174-1183.
27. Ray, W.H., 1981. Advanced Process Control, McGraw Hill, New York.
28. Rush, E., Drews, TO., Ma, D.L., Alkire, R.C., Braatz, R.D., 2003. Nonlinear feedback control of a coupled kinetic Monte Carlo-finite difference simulation. In: Proceedings of the IFAC Symposium on Advanced Control of Chemical Processes, Hong Kong, pp. 597-602
29. Sematech, 2003. International Technology Roadmap for Semiconductors, International Sematech.
30. Tsukagoshi, K., Yoneya, N., Uryu, S., Aoyagi, Y., Kanda, A., Ootuka, Y., Alphenaar, B.W., 2002. Carbon nanotube devices for nanoelectronics. Physica B-Condensed Matter 323, 107-114.