Reduced-Order Model for Dynamic Optimization of Pressure Swing Adsorption
- Carnegie Mellon Univ., Pittsburgh, PA
The last few decades have seen a considerable increase in the applications of adsorptive gas separation technologies, such as pressure swing adsorption (PSA). From an economic and environmental point of view, hydrogen separation and carbon dioxide capture from flue gas streams are the most promising applications of PSA. With extensive industrial applications, there is a significant interest for an efficient modeling, simulation, and optimization strategy. However, the design and optimization of the PSA processes have largely remained an experimental effort because of the complex nature of the mathematical models describing practical PSA processes. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together and high nonlinearities arising from non-isothermal effects. The computational effort required to solve such systems is usually quite expensive and prohibitively time consuming. Besides this, stringent product specifications, required by many industrial processes, often lead to convergence failures of the optimizers. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either design or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Sophisticated optimization strategies have been developed and applied to PSA systems with significant improvement in the performance of the process. However, most of these approaches have been quite time consuming. This gives a strong motivation to develop cost-efficient and robust optimization strategies for PSA processes. Moreover, in case of flowsheet optimization, if dynamic PSA models are incorporated with other steady state models in the flowsheet then it will require much faster approaches for integrated optimization.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR
- Sponsoring Organization:
- USDOE - Office of Fossil Energy (FE)
- OSTI ID:
- 934620
- Report Number(s):
- DOE/NETL-IR-2008-030; NETL-TPR-1744
- Country of Publication:
- United States
- Language:
- English
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