(463f) A Process Systems Engineering Approach to the Discovery of New Catalysts: Optimization of Microkinetic Models

In this work, we present an optimization of microkinetic models (OMM) approach that integrates the predictive power of microkinetic models with multivariate optimization to design new catalysts enabling the transformation of biomass into desired products. We leverage optimization to simultaneously identify both the i) catalysts and ii) the reaction design variables that maximize a desired design objective. To do this, we build upon previous advances (e.g., see [1]) in reaction network generation and theoretical estimation of kinetic parameters to develop microkinetic models. Further, we a priori compute the activation energy of each elementary reaction that occurs on the surface of the catalyst as a function of catalyst descriptors such as pore size and site acidity [1]. We then simultaneously optimize both the reactor design variables as well as catalyst descriptors to maximize a techno-economic design objective.

We apply the OMM approach to the design of optimal Bronsted acid zeolite catalysts for oligomerization. We use a general microkinetic model framework described in [1]. We then use a two-phase approach for the identification of optimal catalysts, akin to the work of [2]. In the first phase, we use continuous optimization to simultaneously identify the optimal catalyst descriptors and the reaction conditions that achieve a specific design objective. In the second step, the optimal catalyst descriptors are used to identify the best-fit existing material.

The optimal catalyst descriptors identified using OMM can guide the synthesis of new catalysts. OMM is a systematic and general approach that leverages the power of optimization and predictive mechanistic modeling to guide the selection and synthesis of catalysts and is applicable across a range of applications.

[1] Vernuccio, S., Bickel, E. E., Gounder, R., Broadbelt L. J.. (2019). ACS Catal. 9, pp. 8996–9008

[2] Bardow, A., Steur, K., Gross, J. (2010). Industrial & Engineering Chemistry Research, 49(6)