ArticleScientific reports2026
Interpretable modeling to forecast CO
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
1 author.
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Abstract
The unintended formation of solid carbon dioxide during the cryogenic processing of natural gas introduces severe operational hazards, pipeline blockages, and financial losses. To address this critical challenge, this study aims to develop a highly accurate, explainable data-driven framework capable of forecasting frosting temperatures based on operating pressures and mixture compositions. By utilizing a comprehensive dataset of 430 experimental data points comprising molar concentrations of methane, carbon dioxide, nitrogen, and ethane, ten distinct machine learning algorithms were trained and rigorously evaluated. The methodology specifically contrasted traditional linear techniques with advanced non-linear ensemble algorithms to identify the most robust predictive tool. Results clearly demonstrate the quantitative superiority of tree-based ensemble methods. Notably, the CatBoost algorithm emerged as the optimal model, achieving exceptional predictive accuracy with a coefficient of determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.