Multi-objective optimization of hydrocracking processes using graph neural differential equations

doi:10.1016/j.cherd.2025.09.042

Multi-objective optimization of hydrocracking processes using graph neural differential equations

2025-10-04

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Hariprasad Kodamana *

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Manojkumar Ramteke

DOI：10.1016/j.cherd.2025.09.042

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Abstract

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• This study introduces Graph Neural Differential Equations model for hydrocracking. • Graph Convolution Networks with Graph Differential Equation solver for optimization. • Model captures the dynamic changes in chemical reactions along the catalyst bed. • Utilized Non-dominated sorting Genetic Algorithm for multi-objective optimization.

Multi-objective optimization of hydrocracking processes using graph neural differential equations

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