Computational Research Challenges and Opportunities for the Optimization of Fossil Energy Power Generation System Page: 3 of 10
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combustion and gasification modeling; comprehensive physical properties, thermodynamics, phase and chemical equilibrium
relations, and reaction kinetics for gas cleanup modeling; and an extensive library of heat exchange and rotating equipment
models for simulating combined cycles. Aspen Plus also provides both the SM and EO solution approaches
Steady-state process optimization gives rise to nonlinear programming problems (NLP) with constraints (equality or
inequality). Many commercial process simulators provide a sequential quadratic programming (SQP) method for continuous
variable optimization. SQP solvers allow the creation of a number of NLP algorithms based on Newton steps. Moreover,
these solvers have been shown to require the fewest function evaluations and they can be tailored to a broad range of process
optimization problems with different structure. Large-scale optimization algorithms for NLPs with several thousand
variables may be needed to augment the current SQP optimization methods in process simulators which are equipped to
handle only up to 100 variables or so. One potential alternative for solving much larger models is the new class of interior
point methods (Wachter and Biegler, 2006). Finally, considerable opportunity exists for additional research on global
optimization methods (Sahinidis, 1996), since nonconvexities in the design problems are likely to yield suboptimal solutions
since the corresponding bounds for the variables are rather loose in these problems. Global optimizers find the "best
optimum" when multiple local solutions exist, for example in applications such as the consumption of freshwater in
integrated process water systems (Karuppiah and Grossmann, 2006).
High-Fidelity Co-Simulation
To improve the accuracy of FE system design, steady-state equipment models evolve in complexity from lumped-parameter
to spatially distributed representations based on partial differential equations (PDEs) in multiple dimensions. Due to the need
for accurate spatial discretizations for fluid flow, heat and mass transfer, and reacting systems, optimization problems
involving PDE formulations are often orders of magnitude larger than typical optimization applications. In addition, the
integration of high-fidelity PDE-based equipment models (such as computational fluid dynamics (CFD) models) with overall
process models leads to the creation of very large models for process optimization.The Advanced Process Engineering Co-Simulator
(APECS) developed at the DOE's National Energy
Technology Laboratory (NETL) provides
process/equipment co-simulation capabilities (Zitney et
al., 2006). The hierarchy of equipment models ranges
from high-fidelity CFD models to custom engineering
models (CEMs) to fast reduced-order models (ROMs)
based on pre-computed CEM or CFD results. At
NETL, system analysts typically use APECS to run
power plant co-simulations coupling the steady-state
process simulator, Aspen Plus, with CFD models based
on FLUENT@ (ANSYS/Fluent, 2006), a leading
software package for comprehensive flow analysis. As
shown in Figure 2, the APECS integration framework
uses the process industry-standard CAPE-OPEN
(www.colan.org) software interfaces to provide plug-
and-play interoperability between process simulation
and equipment simulations (Zitney, 2004a).
The APECS process/CFD co-simulation technology
enables process design engineers to analyze and
optimize power plant performance with respect to fluid
flow in key equipment items, such as combustors,
gasifiers, syngas coolers, steam and gas turbines, heat
recovery steam generators, and fuel cells. At NETL,
system analysts, oftentimes in collaboration with R&D
partners (e.g., ALSTOM Power), are applying APECS
to a wide variety of advanced power generation
systems, ranging from small fuel cell systems to
commercial-scale power plants (Figure 3).7W - - V,
ID
Aspen Plus Process Model CFD Viewer
co
Integration Controller
(CAPE-OPEN Interface )
Custom Reduced
FLUENT CFD Device Model Order Model
Configuration Configuration Configuration
Wizard Wizard Wizard
Equipment Model Database
Figure 2. APECS Co-Simulator
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Zitney, S. E. Computational Research Challenges and Opportunities for the Optimization of Fossil Energy Power Generation System, article, June 1, 2007; (https://digital.library.unt.edu/ark:/67531/metadc889818/m1/3/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.