Physics-Informed Neural Network with NSGA II and Levenberg–Marquardt Method for Kinetic Modeling in Heavy Oil Hydrocracking
Bibliographic record
Abstract
Accurately modeling reaction kinetics in heavy oil hydrocracking is essential for optimizing reactor performance and improving product distribution predictions. This study proposes a novel hybrid framework that integrates a physics-informed neural network (PINN) with nondominated sorting genetic algorithm II (NSGA-II) and Levenberg–Marquardt (LM) optimization method to achieve fast and accurate estimation of kinetic parameters. Unlike conventional approaches, the proposed method combines global and local search: NSGA-II generates high-quality initial parameter estimates, while LM efficiently refines them, ensuring convergence within 300 epochs. This hybrid framework leverages a neural network to model time-evolving behavior, while a Runge–Kutta–based solver enforces reaction kinetics, enabling robust kinetic parameter estimation under physical constraints. Four kinetic models previously proposed in hydrocracking research were implemented as physics constraints and systematically evaluated using the PINN framework. Among them, the most detailed, referred to here as model 4, emerged as the most comprehensive and accurate, capturing all major saturates, aromatics, resins, and asphaltenes (SARA) conversions and byproduct formation (gas and coke). Building upon this, a refined 10-parameter kinetic model was proposed by excluding three low-sensitivity reaction parameters. The simplified model preserved all dominant pathways and demonstrated excellent predictive accuracy across four temperatures (360–400 °C), total error (data + physics MSE on mass fractions) was on the order of 10 –3 to 10 –2 across training, validation, and testing, with R 2 between 0.93 and 0.99. To prevent overfitting and improve generalization, early stopping and a 20% dropout strategy were employed. This study presents a novel application of a hybrid PINN framework that integrates a multiobjective evolutionary algorithm with numerical optimization for kinetic modeling in heavy oil hydrocracking. By embedding physical constraints into the learning process, the framework offers a scalable, interpretable, and accurate approach for estimating reaction parameters and capturing the dynamic behavior of the hydrocracking process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".