Data-driven support vector regression-based hybrid models for prediction of syngas production in the gasification process of biomass
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Ying Yang
Abstract
Biomass gasification represents a thermal and chemical interaction that transforms biomass substances contained in the reactor. Several interconnected factors influence the gasifier’s performance, including the fuel type, reactor design, and operational parameters. A comprehensive recognizing this operational approach is essential for varied consumers, such as individuals interested in the gasifier’s output, reactor manufacturers aiming to optimize their designs, or planners seeking the best-performing gasifier for specific fuel types. Extensive research and development efforts have been devoted to gasification, encompassing both experimental and computational approaches. Computational modeling tools offer significant advantages, enabling users to determine optimal reactor conditions without time-consuming and costly experimentation. Efficiently interpreting the embedded information in gasification modeling necessitates a systematic and logical analysis, a goal pursued in the present study. Data-driven Support Vector Regression (SVR) based hybrid schemes utilizing Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO) were created in this investigation to determine the score of H2 and N2 concentration based on the various input parameters. A strong predictive performance of hybrid schemes, especially SVGT, was confirmed by a coefficient of determination (R2) of 0.994 and 0.999 in the case of yielded N2 and H2 estimation. The technique can produce reliable input data for appraisal of costs and benefits and life cycle ecological assessments, allowing politicians and financiers to make more transparent decisions.
Acknowledgments
I 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: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “ Ying Yang “. Also the first draft of the manuscript was written by Ying 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 author declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The author do not have permissions to share data.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cppm-2025-0016).
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