Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. Office of Basic Energy Sciences, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 25,847-25,863 : digital, PDF file
- Additional Creators:
- University of Wisconsin
United States. Department of Energy. Office of Basic Energy Sciences
United States. Department of Energy. Office of Scientific and Technical Information
- Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L<sub>2</sub> regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization. A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO<sub>2</sub> on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.
- Published through SciTech Connect.
Journal of Physical Chemistry. C 121 46 ISSN 1932-7447 AM
Srinivas Rangarajan; Christos T. Maravelias; Manos Mavrikakis.
- Funding Information:
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