Utilizing a Naïve Bayes model for gasification process prediction: enhancing efficiency and sustainability
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Meixiang Wang
Abstract
The presented study introduces an advanced predictive modeling framework designed to predict CH4 and C2Hn yields in the gasification process, in which a method is traditionally employed to convert carbonaceous feedstocks into industrially valuable gases. Accurate prediction of gas yields is essential for improving operational efficiency and promoting environmental sustainability. The proposed approach seeks to advance gasification through machine learning models that suggest improved efficiency, cost-effectiveness, and precision. Such models support real-time decision-making and adaptability across diverse operational conditions, while also elevating safety by minimizing dependencies on conventional experimentation and expanding the scope of controllable parameters. The Naïve Bayes algorithm is employed to model complex relationships between influential input variables and gas production outcomes. To further boost predictive accuracy, the NB model is integrated with two metaheuristic optimizers, comprising the Weevil Damage Optimization Algorithm (WDOA) and the Crystal Structure Algorithm (CSA). These integrated approaches significantly elevate model precision and facilitate process optimization. Among the developed models, the CSA-enhanced version (NBCS) delivered the maximum performance and achieved a training R2 of 0.981 for CH4 and 0.976 for C2Hn. More importantly, test R2 values of 0.978 for CH4 and 0.955 for C2Hn confirmed the NBCS model’s strong generalization capability, outperforming the baseline NB model, which yielded test R2 values of 0.949 and 0.955, respectively. In terms of statistical metrics, including MSE and RMSE, both NBWD and NBCS models outlined substantial improvements over the standalone NB model in predicting gasification outputs.
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 “Meixiang Wang”. Also the first draft of the manuscript was written by Meixiang Wang 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 does not have permissions to share data.
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