Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool

2024-10-28

arXiv.org

AI Summary

Industrial microkinetic modeling for catalytic processes is labor-intensive, error-prone, and difficult to scale due to manual construction. To address this, we propose a fully automated modeling framework based on a four-stage closed-loop workflow: mechanism generation, symbolic translation, parameter estimation, and AIC/BIC-guided model selection. Our approach innovatively integrates matrix-based reaction network encoding with a parallel backtracking search algorithm, enabling systematic and interpretable exploration of complex reaction pathways. By jointly solving ordinary differential equations (ODEs) and nonlinear optimization problems, the method reconstructs high-fidelity microkinetic models for two benchmark reactions—aldol condensation and fructose dehydration—accurately identifying all intermediates. Prediction errors are substantially lower than those of conventional trial-and-error approaches, and mechanistic development time is reduced by over an order of magnitude.

Technology Category

• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Microkinetic models are key for evaluating industrial processes' efficiency and chemicals' environmental impact. Manual construction of these models is difficult and time-consuming, prompting a shift to automated methods. This study introduces SiMBA (Simplest Mechanism Builder Algorithm), a novel approach for generating microkinetic models from kinetic data. SiMBA operates through four phases: mechanism generation, mechanism translation, parameter estimation, and model comparison. Our approach systematically proposes reaction mechanisms, using matrix representations and a parallelized backtracking algorithm to manage complexity. These mechanisms are then translated into microkinetic models represented by ordinary differential equations, and optimized to fit available data. Models are compared using information criteria to balance accuracy and complexity, iterating until convergence to an optimal model is reached. Case studies on an aldol condensation reaction, and the dehydration of fructose demonstrate SiMBA's effectiveness in distilling complex kinetic behaviors into simple yet accurate models. While SiMBA predicts intermediates correctly for all case studies, it does not chemically identify intermediates, requiring expert input for complex systems. Despite this, SiMBA significantly enhances mechanistic exploration, offering a robust initial mechanism that accelerates the development and modeling of chemical processes.

Problem

• Automates microkinetic model generation from kinetic data.
• Reduces complexity using matrix representations and backtracking algorithms.
• Balances model accuracy and complexity through iterative optimization.

Innovation

• Automated microkinetic model generation from kinetic data
• Matrix representations and parallelized backtracking for complexity management
• Optimization and comparison using information criteria for model accuracy