Modeling the CO and CO2 output in the gasification process using optimized hybrid machine learning models
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Dandan Song
and
Tao Yang
Abstract
Gasification modeling involves developing and evaluating computational and mathematical frameworks to replicate and forecast the behavior of gasification processes. Among these frameworks, the Decision Tree (DT) model is particularly popular for its capacity to handle both categorical and numerical data while maintaining interpretability. This characteristic shed light on the key factors affecting gasification outcomes. This hybrid strategy harnessed the advantages of DTs for predictive modeling, along with enhancement methods to refine the gasification process. According to the performance metrics, the DTAO hybrid model consistently excelled compared to others, recording the highest R2 values of 0.989 (CO) and 0.985 (CO2), the lowest RMSE values of 0.996 (CO) and 0.797 (CO2), as well as the lowest OBJ values of 0.935 (CO) and 0.793 (CO2). This confirms its exceptional accuracy and reliability in gasification prediction.
Funding source: Key Scientific Research Project of Higher Education Institutions in Henan Province: Research on SOH estimation model for lithium batteries based on chaotic state
Award Identifier / Grant number: 24B48005
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not necessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Dandan Song and Tao Yang”. Also, the first draft of the manuscript was written by Dandan Song. Tao Yang commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: Key Scientific Research Project of Higher Education Institutions in Henan Province Research on SOH estimation model for lithium batteries based on chaotic state, Project No. 24B48005.
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Data availability: The authors do not have permissions to share data.
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