Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

Srinivas Rangarajan, Christos T. Maravelias, Manos Mavrikakis

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)25847-25863
Number of pages17
JournalJournal of Physical Chemistry C
Volume121
Issue number46
DOIs
StatePublished - Nov 22 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Energy(all)
  • Physical and Theoretical Chemistry
  • Surfaces, Coatings and Films

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