Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool
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arXiv
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| Format: | Preprint |
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2024
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| author | Servia, Miguel Ángel de Carvalho Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio |
| author_facet | Servia, Miguel Ángel de Carvalho Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool Servia, Miguel Ángel de Carvalho Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio Computational Engineering, Finance, and Science Symbolic Computation 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. |
| title | Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool |
| topic | Computational Engineering, Finance, and Science Symbolic Computation |
| url | https://arxiv.org/abs/2410.21205 |