Advanced Kinetics and Reaction Engineering
Analysis of Transient Kinetic Degree of Rate Control on CO Oxidation
Description:
Chemical reaction kinetics is an essential analysis conducted on reaction rates and profiles to better understand the behavior of a chemical system with respect to some predefined conditions. These kinetic insights play an important role in informing and improving chemical reactor design as well as in refining and optimizing catalyst selection. In deriving useful kinetic information, the concept of ‘Rate-Determining Step (RDS)’ is ubiquitous in characterizing the step in the reaction mechanism that when perturbed, would have the most significant effect on the kinetics of the overall reaction. There are many ways to characterize a significant kinetic effect and consequently, there exists several different qualitative and qualitative approaches to identifying the RDS; some of which have significant limitations and shortcomings.
Out of all the different approaches of obtaining the RDS, Campbell’s degree of rate control shows to give the most reliable description of which steps would be the most kinetically relevant. Using a numerical finite difference approach, we can easily perform transient rate control analysis and obtain information on which steps are kinetically relevant and would, if accelerated, positively impact the overall net rate of reaction. In the example used, we were able to see that before reaching steady state, there was significant changes in the different step’s kinetic relevance (degrees of rate control) of which can be attributed to the changes in surface coverage. The transient degree of rate control tool can therefore be used to fine tune the process of identifying a selective catalyst or catalyst additive that would help improve the overall reaction kinetics as needed.
Nature of Project :
-Individual Project
-Advanced Kinetics
-Transient Simulations
-Reaction Analysis
Concepts Utilised:
-Degree of Rate Control
-Rate Limiting Step
-Numerical Analysis
-Sensitivity Analysis
Final Report*:
Advanced Kinetics_Final Report_KennethKusima.pdf5th i-CoMSE School: Machine learning in Molecular Science
Institute for Computational Molecular Science Education (i-CoMSE) | University of Minnesota
July 09 - 14, 2023
Machine Learned Corrections to Transient Micro-Kinetic Models
Description:
Transient mean-field micro-kinetic (MF-MK) modeling is a powerful approach to study, simulate, and forecast the reaction kinetics of heterogeneously catalyzed reactions. Using elementary steps to represent molecular-level interactions among chemical species allows for the exploration of the reaction mechanism to provide detailed kinetic information that aids in the interpretation of experimental findings. Typical MK models rely on the mean field approximation that disregards lateral interactions between surface adsorbates. In many cases, such interactions have significant effects on overall reaction kinetics.
Kinetic Monte Carlo (kMC) simulations produce coverage and reaction rate information that accounts for lateral surface adsorbate interactions. Nevertheless, kMC simulations are computationally expensive and require meticulous setup for accurate results.
Using machine learning (ML), we have been able to develop an improved MK model that uses a machine learned correction factor to obtain kMC-like results from the MF-MK model simulations at different coverages. The resulting ML-MK model appears to match closely with transient kMC simulation results. Consequently, our enhanced ML-MK model can predict the effects of surface adsorbate interactions that cannot be captured in a MF-MK model, while also maintaining an easy setup and minimal computational effort.
Nature of Project :
-Individual Project
-Artificial Intelligence
-Machine Learning
-Computational Research
Concepts Utilised:
-Reaction Kinetics
-Kinetic Monte Carlo
-Micro-Kinetics
-Supervised Learning
Final Poster*:
Final_Poster_Kenneth_iCOMSE_2023_size_24_36.pdfENERGY RESEARCH DAY 2023
UNIVERSITY OF HOUSTON | UH ENERGY
Dynamic Transient Micro-Kinetic Modelling Toolkit
Description:
Research Theme
• Micro-Kinetic Modelling is an important and popular simulation technique used to obtain kinetic information of which help instruct reactor modelling through:
• Providing insights into different reaction mechanism of which help to understand underlying chemistry
• Helping determine the design of catalysts defined mostly by the behavior of the kinetically relevant steps
• Establishing a relationship between system conditions and reaction rate of which help inform the generation of a predictive kinetic model
• Controlled dynamic (periodic) operation of reactors for certain reactions has been shown to influence the state of the catalyst surface in a way that leads to higher rates of production. These dynamically enhanced rates offer the opportunity to establish higher yields of which help motivate the generation of dynamically operated reactors. A toolkit that can model such behavior offers to be incredibly useful in the growth of these new kinds of reactors.
• The growth of Artificial Intelligence (AI) has allowed for access of strong predicative and optimization techniques that only require large datasets. Machine Learning (ML) has been previously used to improve the fitting of theoretical results to match those obtained experimentally
Technical Issues
An Object-Oriented Python Based Multipurpose Toolkit has been made to achieve the needs mentioned in the Research Theme, nevertheless, the following still act as current ongoing issues:
• Fitting experimental results to the microkinetic model data requires robust fitting techniques to make sure the toolkit can handle various reaction and surface conditions
• In addition, a rigorous uncertainty analysis needs to exist to make sure the fitting performed can be trusted
• Make sure fitting can be done on periodic data under different dynamic conditions
The toolkit is intended to be a new generalized way to analyze reaction kinetics under dynamic conditions, but before distribution of the software, important work needs to be done to:
• Establish a user-friendly manual with in-depth explanations and tutorials
• Provide a thorough description of possible software capabilities as well as limitations
Nature of Project :
-Individual Project
-Software Engineering
-Computational Research
Concepts Utilised:
-Object Oriented Programming
-Computational Kinetics
-Differential Equations
-MicroKinetic Modelling
Final Poster*:
Kenneth_Kusima_2023_ERD_Poster.pdf