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September 18, 2025Open Access

A Machine-Learning Pipeline for Predicting Methanol Yield in a Double-Pass Plug-Flow Reactor Using a Synthetic Langmuir–Hinshelwood Dataset

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Authors

AAAnthony Asuquo

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Implication

A machine-learning pipeline predicts methanol yield in a double-pass reactor, suggesting improved reactor optimization through synthetic data analysis.

Key Points

  • The machine-learning model achieved R² = 0.9897 for predicting methanol yield on an original scale.
  • Using 5,000 data points from a synthetic Langmuir–Hinshelwood dataset, the Random Forest model significantly outperformed others.
  • Key operating conditions like temperature showed strong correlations, guiding the model's performance and predictions.
  • The tool, deployed via FastAPI, enables real-time predictions for sustainable methanol production, highlighting its practical application.

Cite This Study

Anthony Asuquo (2025) studied this question.

synapsesocial.com/papers/68d433aa713b0b5dfea7319bhttps://doi.org/10.26434/chemrxiv-2025-j2l4q
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