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Title: Innovative computational tools and models for the design, optimization and control of carbon capture processes

Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
Report Number(s):
Resource Type:
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; Modeling, Simulation, Optimization, Carbon Capture

Citation Formats

Miller, David C. Innovative computational tools and models for the design, optimization and control of carbon capture processes. United States: N. p., 2017. Web. doi:10.1002/9781119106418.ch12.
Miller, David C. Innovative computational tools and models for the design, optimization and control of carbon capture processes. United States. doi:10.1002/9781119106418.ch12.
Miller, David C. Sat . "Innovative computational tools and models for the design, optimization and control of carbon capture processes". United States. doi:10.1002/9781119106418.ch12.
title = {Innovative computational tools and models for the design, optimization and control of carbon capture processes},
author = {Miller, David C.},
abstractNote = {},
doi = {10.1002/9781119106418.ch12},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Apr 01 00:00:00 EDT 2017},
month = {Sat Apr 01 00:00:00 EDT 2017}

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  • Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and universities that is developing, demonstrating, and deploying a suite of multi-scale modeling and simulation tools. One significant computational tool is FOQUS, a Framework for Optimization and Quantification of Uncertainty and Sensitivity, which enables basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to rapidly synthesize and optimize a process and determine the level of uncertainty associated with the resulting process. The overall approachmore » of CCSI is described with a more detailed discussion of FOQUS and its application to carbon capture systems.« less
  • With increasing demand placed on power generation plants to reduce carbon dioxide (CO2) emissions, processes to separate and capture CO2 for eventual sequestration are highly sought after. Carbon capture processes impart a parasitic load on the power plants; it is estimated that this would increase the cost of electricity from existing pulverized coal plants anywhere from 71-85 percent [1]. The National Energy and Technology Lab (NETL) is working to lower this to below a 30 percent increase. To reach this goal, work is being done not only to accurately simulate these processes, but also to leverage those accurate and detailedmore » simulations to design optimal carbon capture processes. The major challenges include the lack of accurate algebraic models of the processes, computationally costly simulations, and insufficiently robust simulations. The first challenge bars the use of provable derivative-based optimization algorithms. The latter two can either lead to difficult or impossible direct derivative-free optimization. To overcome these difficulties, we take a more indirect method to solving this problem by, first, generating an accurate set of algebraic surrogate models from the simulation then using derivative-based solvers to optimize the surrogate models. We developed a method that uses derivative-based and derivative-free optimization alongside machine learning and statistical techniques to generate the set of low-complexity surrogate models using data sampled from detailed simulations. The models are validated and improved through the use of derivative-free solvers to adaptively sample new simulation points. The resulting surrogate models can then be used in a superstructure-based process synthesis and solved using derivative-based methods to optimize carbon capture processes.« less
  • Sanitary landfilling is the solid waste disposal technique most widely practiced today. In conventional landfill design, the leachate generated in the landfill is contained by a low conductivity liner constructed at the base of the landfill. However, due to the inherent limitations of natural materials and the inevitable imperfections of installing plastic or rubber liners, escape of leachate is inevitable in conventional landfills. The concept of the artesian landfill system--in which the downward movement of leachate is prevented by reversing the hydraulic gradient so that seepage occurs into, and not out of, the landfill--is illustrated in this paper. A two-dimensional,more » transient saturated-unsaturated finite element flow model developed in the paper demonstrates that the reverse gradient eliminates or limits the loss of leachate from the landfill even if the integrity of the landfill liner is imperfect or deteriorates over time. A geometric programming model is also developed for the least-cost design of artesian landfill system components based on the capital and operating costs of the system.« less
  • This paper presents a finite element solution of an inverse solidification design problem. It is based on the previous work on an adjoint method with a functional optimization scheme for the solution of inverse thermal convection problems with overspecified thermal boundary conditions. An inverse calculation is performed here for directional solidification processes to find the optimal heat flux at the mold wall boundary on both the solid and liquid mold sides. The objective is to achieve desired velocity and heat flux histories at the solid-liquid interface. The specification of the growth velocity and freezing interface heat fluxes considers the microstructuralmore » implications on the casting product and the morphological stability requirements of the freezing interface. An example of solidification in a rectangular mold with a planar interface growth is shown.« less
  • This presentation reports development of advanced computational tools to accelerate next generation technology development. These tools are to develop an optimized process using rigorous models. They include: Process Models; Simulation-Based Optimization; Optimized Process; Uncertainty Quantification; Algebraic Surrogate Models; and Superstructure Optimization (Determine Configuration).