An Intelligent System for Reaction Kinetic Modeling and Catalyst Design

Abstract

The continuing development of high throughput experiments (HTEs) in catalysis has dramatically increased the amount of data that can be collected in relatively short periods of time. Even when HTEs can afford “Edisonian” discovery, how can the increasing amounts of data be converted to knowledge to guide the next search in the vast design space of catalytic materials? To address this question, we recently proposed a catalyst design architecture that uses detailed kinetic models. In this paper, we describe Reaction Modeling Suitea rational, automated, and intelligent environment, based on systems, artificial intelligence, and optimization techniques that aid the development of kinetic models. We demonstrate its utility in developing a kinetic model for propane aromatization on zeolite. We also show the proof-of-concept of how a genetic algorithm-based search strategy can be used to search for kinetic parameters that correspond to an improved catalyst.

Rational Asymmetric Catalyst Design, Intensification and Modeling

“…In homogeneous catalyst design control of activity, ligand type, concentration of co-catalyst, molecular weight, tacticity (depends upon equilibrium), etc are also important. [6]. The key steps involved in this model building are generation of the simplest plausible reaction mechanism, translation of the reaction mechanism to a computationally tractable mathematical model, solving the model to estimate the parameters in light of high throughput and/or insufficient experimental data, refining the model to better fit the data by altering the mechanism, suggesting new experiments that could help in discriminate among multiple models.…”

“…The key steps involved in this model building are generation of the simplest plausible reaction mechanism, translation of the reaction mechanism to a computationally tractable mathematical model, solving the model to estimate the parameters in light of high throughput and/or insufficient experimental data, refining the model to better fit the data by altering the mechanism, suggesting new experiments that could help in discriminate among multiple models. These software tools needed state of the art features for specific purposes [6]:…”

“…Reaction Modeling Suite (RMS) was developed by Purdue University, USA [6]. The quantitative kinetic description of a reaction mechanism involves specification of a differential and algebraic equation for each reaction component.…”

“…Advances in surface science enable data manipulation of individual atoms on the catalyst surface with experiments provides initial guess towards systematic design. Number of model based catalyst design strategies were reported in open literature like use of qualitative reasoning and knowledge-based systems [2,3], computational models [4][5][6] and detailed microkinetic modeling for catalytic systems [7]. In order to get the job done, compositional analysis of physical, chemical, and/or biological properties and validation of appropriate molecular structure (robust forward model linked with material description and knowledge extraction from experiment).…”

“…These tools allow rigorous analysis of multiple reaction mechanisms in the light of experimental data. Katare et al [6] demonstrated kinetic model development for propane aromatization on H-ZSM-5 zeolite catalyst with a proof-ofconcept in his studies.…”