Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems
Abstract
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 (L2 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 CO2 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.
- Authors:
-
[1];
[1]
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States); University of Wisconsin-Madison, WI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Contributing Org.:
- Pacific Northwest National Laboratory (PNNL); Argonne National Laboratory (ANL); National Energy Research Scientific Computing Center (NERSC)
- OSTI Identifier:
- 1408149
- Alternate Identifier(s):
- OSTI ID: 1421604; OSTI ID: 1507711; OSTI ID: 1994820
- Grant/Contract Number:
- FG02-05ER15731
- Resource Type:
- Published Article
- Journal Name:
- Journal of Physical Chemistry. C
- Additional Journal Information:
- Journal Name: Journal of Physical Chemistry. C Journal Volume: 121 Journal Issue: 46; Journal ID: ISSN 1932-7447
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Rangarajan, Srinivas, Maravelias, Christos T., and Mavrikakis, Manos. Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems. United States: N. p., 2017.
Web. doi:10.1021/acs.jpcc.7b08089.
Rangarajan, Srinivas, Maravelias, Christos T., & Mavrikakis, Manos. Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems. United States. https://doi.org/10.1021/acs.jpcc.7b08089
Rangarajan, Srinivas, Maravelias, Christos T., and Mavrikakis, Manos. Thu .
"Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems". United States. https://doi.org/10.1021/acs.jpcc.7b08089.
@article{osti_1408149,
title = {Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems},
author = {Rangarajan, Srinivas and Maravelias, Christos T. and Mavrikakis, Manos},
abstractNote = {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 (L2 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 CO2 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.},
doi = {10.1021/acs.jpcc.7b08089},
journal = {Journal of Physical Chemistry. C},
number = 46,
volume = 121,
place = {United States},
year = {Thu Nov 09 00:00:00 EST 2017},
month = {Thu Nov 09 00:00:00 EST 2017}
}
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https://doi.org/10.1021/acs.jpcc.7b08089
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