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Title: CAPE-OPEN compliant stochastic modeling and reduced-order model coputation capaability for APECS system. ORIGINAL TITLE: CAPE-OPEN compliant stochastic modeling capability

Abstract

APECS (Advanced Process Engineering Co-Simulator) is an integrated software suite that combines the power of process simulation with high-fidelity, computational fluid dynamics (CFD) for improved design, analysis, and optimization of process engineering systems. The APECS system uses commercial process simulation (e.g., Aspen Plus) and CFD (e.g., FLUENT) software integrated with the process-industry standard CAPE-OPEN (CO) interfaces. This breakthrough capability allows engineers to better understand and optimize the fluid mechanics that drive overall power plant performance and efficiency. The focus of this paper is the CAPE-OPEN complaint stochastic modeling and reduced order model computational capability around the APECS system. The usefulness of capabilities is illustrated with coal fired, gasification based, FutureGen power plant simulation. These capabilities are used to generate efficient reduced order models and optimizing model complexities.

Authors:
; ; ;
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research; National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States)
Sponsoring Org.:
USDOE Assistant Secretary for Fossil Energy (FE)
OSTI Identifier:
1014399
Report Number(s):
NETL-TPR-1956
TRN: US201111%%320
Resource Type:
Conference
Resource Relation:
Conference: 2007 AIChE Annual Meeting, Salt Lake City, UT, November 4-9, 2007
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; COAL; COMPUTERIZED SIMULATION; DESIGN; EFFICIENCY; ENGINEERS; FLUID MECHANICS; GASIFICATION; OPTIMIZATION; PERFORMANCE; POWER PLANTS; SIMULATION

Citation Formats

Diwekar, U., Shastri, Y., Subramanayan, K., and Zitney, S. CAPE-OPEN compliant stochastic modeling and reduced-order model coputation capaability for APECS system. ORIGINAL TITLE: CAPE-OPEN compliant stochastic modeling capability. United States: N. p., 2007. Web.
Diwekar, U., Shastri, Y., Subramanayan, K., & Zitney, S. CAPE-OPEN compliant stochastic modeling and reduced-order model coputation capaability for APECS system. ORIGINAL TITLE: CAPE-OPEN compliant stochastic modeling capability. United States.
Diwekar, U., Shastri, Y., Subramanayan, K., and Zitney, S. 2007. "CAPE-OPEN compliant stochastic modeling and reduced-order model coputation capaability for APECS system. ORIGINAL TITLE: CAPE-OPEN compliant stochastic modeling capability". United States. doi:.
@article{osti_1014399,
title = {CAPE-OPEN compliant stochastic modeling and reduced-order model coputation capaability for APECS system. ORIGINAL TITLE: CAPE-OPEN compliant stochastic modeling capability},
author = {Diwekar, U. and Shastri, Y. and Subramanayan, K. and Zitney, S.},
abstractNote = {APECS (Advanced Process Engineering Co-Simulator) is an integrated software suite that combines the power of process simulation with high-fidelity, computational fluid dynamics (CFD) for improved design, analysis, and optimization of process engineering systems. The APECS system uses commercial process simulation (e.g., Aspen Plus) and CFD (e.g., FLUENT) software integrated with the process-industry standard CAPE-OPEN (CO) interfaces. This breakthrough capability allows engineers to better understand and optimize the fluid mechanics that drive overall power plant performance and efficiency. The focus of this paper is the CAPE-OPEN complaint stochastic modeling and reduced order model computational capability around the APECS system. The usefulness of capabilities is illustrated with coal fired, gasification based, FutureGen power plant simulation. These capabilities are used to generate efficient reduced order models and optimizing model complexities.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2007,
month = 1
}

Conference:
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  • APECS (Advanced Process Engineering Co-Simulator) is an integrated software suite that combines the power of process simulation with high-fidelity, computational fluid dynamics (CFD) for improved design, analysis, and optimization of process engineering systems. The APECS system uses commercial process simulation (e.g., Aspen Plus) and CFD (e.g., FLUENT) software integrated with the process-industry standard CAPE-OPEN (CO) interfaces. This breakthrough capability allows engineers to better understand and optimize the fluid mechanics that drive overall power plant performance and efficiency. The focus of this paper is the CAPE-OPEN complaint stochastic modeling and reduced order model computational capability around the APECS system. The usefulnessmore » of capabilities is illustrated with coal fired, gasification based, FutureGen power plant simulation. These capabilities are used to generate efficient reduced order models and optimizing model complexities.« less
  • Engineering simulations of coal gasifiers are typically performed using computational fluid dynamics (CFD) software, where a 3-D representation of the gasifier equipment is used to model the fluid flow in the gasifier and source terms from the coal gasification process are captured using discrete-phase model source terms. Simulations using this approach can be very time consuming, making it difficult to imbed such models into overall system simulations for plant design and optimization. For such system-level designs, process flowsheet software is typically used, such as Aspen Plus® [1], where each component where each component is modeled using a reduced-order model. Formore » advanced power-generation systems, such as integrated gasifier/gas-turbine combined-cycle systems (IGCC), the critical components determining overall process efficiency and emissions are usually the gasifier and combustor. Providing more accurate and more computationally efficient reduced-order models for these components, then, enables much more effective plant-level design optimization and design for control. Based on the CHEMKIN-PRO and ENERGICO software, we have developed an automated methodology for generating an advanced form of reduced-order model for gasifiers and combustors. The reducedorder model offers representation of key unit operations in flowsheet simulations, while allowing simulation that is fast enough to be used in iterative flowsheet calculations. Using high-fidelity fluiddynamics models as input, Reaction Design’s ENERGICO® [2] software can automatically extract equivalent reactor networks (ERNs) from a CFD solution. For the advanced reduced-order concept, we introduce into the ERN a much more detailed kinetics model than can be included practically in the CFD simulation. The state-of-the-art chemistry solver technology within CHEMKIN-PRO allows that to be accomplished while still maintaining a very fast model turn-around time. In this way, the ERN becomes the basis for high-fidelity kinetics simulation, while maintaining the spatial information derived from the geometrically faithful CFD model. The reduced-order models are generated in such a way that they can be easily imported into a process flowsheet simulator, using the CAPE-OPEN architecture for unit operations. The ENERGICO/CHEMKIN-PRO software produces an ERN-definition file that is read by a dynamically linked library (DLL) that can be easily linked to any CAPE-OPEN compliant software. The plug-in unitoperation module has been successfully demonstrated for complex ERNs of coal gasifiers, using both Aspen Plus and COFE process flowsheet simulators through this published CAPE-OPEN interface.« less
  • Many systems involving chemical reactions between small numbers of molecules exhibit inherent stochastic variability. Such stochastic reaction networks are at the heart of processes such as gene transcription, cell signaling or surface catalytic reactions, which are critical to bioenergy, biomedical, and electrical storage applications. The underlying molecular reactions are commonly modeled with chemical master equations (CMEs), representing jump Markov processes, or stochastic differential equations (SDEs), rather than ordinary differential equations (ODEs). As such reaction networks are often inferred from noisy experimental data, it is not uncommon to encounter large parametric uncertainties in these systems. Further, a wide range of timemore » scales introduces the need for reduced order representations. Despite the availability of mature tools for uncertainty/sensitivity analysis and reduced order modeling in deterministic systems, there is a lack of robust algorithms for such analyses in stochastic systems. In this talk, we present advances in algorithms for predictability and reduced order representations for stochastic reaction networks and apply them to bistable systems of biochemical interest. To study the predictability of a stochastic reaction network in the presence of both parametric uncertainty and intrinsic variability, an algorithm was developed to represent the system state with a spectral polynomial chaos (PC) expansion in the stochastic space representing parametric uncertainty and intrinsic variability. Rather than relying on a non-intrusive collocation-based Galerkin projection [1], this PC expansion is obtained using Bayesian inference, which is ideally suited to handle noisy systems through its probabilistic formulation. To accommodate state variables with multimodal distributions, an adaptive multiresolution representation is used [2]. As the PC expansion directly relates the state variables to the uncertain parameters, the formulation lends itself readily to sensitivity analysis. Reduced order modeling in the time dimension is accomplished using a Karhunen-Loeve (KL) decomposition of the stochastic process in terms of the eigenmodes of its covariance matrix. Subsequently, a Rosenblatt transformation relates the random variables in the KL decomposition to a set of independent random variables, allowing the representation of the system state with a PC expansion in those independent random variables. An adaptive clustering method is used to handle multimodal distributions efficiently, and is well suited for high-dimensional spaces. The spectral representation of the stochastic reaction networks makes these systems more amenable to analysis, enabling a detailed understanding of their functionality, and robustness under experimental data uncertainty and inherent variability.« less
  • Abstract not provided.