TY - JOUR
T1 - Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems
AU - Rangarajan, Srinivas
AU - Maravelias, Christos T.
AU - Mavrikakis, Manos
N1 - Funding Information:
This work was supported by DOE-BES, Office of Chemical Sciences (grant DE-FG02-05ER15731). The authors would like to acknowledge Drs. Patricia Rubert-Nason, Suyash Singh, and
Funding Information:
Yunhai Bai for insights and suggestions during the course of this work and preparation of this manuscript. Computational work was performed in part using supercomputing resources from the following institutions: EMSL, a national scientific user facility at Pacific Northwest National Laboratory (PNNL); the Center for Nanoscale Materials (CNM) at Argonne National Laboratory (ANL); and the National Energy Research Scientific Computing Center (NERSC). EMSL is sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL. CNM and NERSC are supported by the U.S. Department of Energy, Office of Science, under contracts DE-AC02-06CH11357 and DE-AC02-05CH11231, respectively.
Funding Information:
This work was supported by DOE-BES, Office of Chemical Sciences (grant DE-FG02-05ER15731). The authors would like to acknowledge Drs. Patricia Rubert-Nason, Suyash Singh, and Yunhai Bai for insights and suggestions during the course of this work and preparation of this manuscript. Computational work was performed in part using supercomputing resources from the following institutions: EMSL, a national scientific user facility at Pacific Northwest National Laboratory (PNNL); the Center for Nanoscale Materials (CNM) at Argonne National Laboratory (ANL); and the National Energy Research Scientific Computing Center (NERSC). EMSL is sponsored by the Department of Energy's Office of Biological and Environmental Research located at PNNL. CNM and NERSC are supported by the U.S. Department of Energy, Office of Science under contracts DE-AC02-06CH11357 and DE-AC02-05CH11231, respectively.
Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/11/22
Y1 - 2017/11/22
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jpcc.7b08089
DO - 10.1021/acs.jpcc.7b08089
M3 - Article
AN - SCOPUS:85035768002
SN - 1932-7447
VL - 121
SP - 25847
EP - 25863
JO - Journal of Physical Chemistry C
JF - Journal of Physical Chemistry C
IS - 46
ER -