Forecasting gasification process results via radial basis function optimization schemes
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Yanling Xu
und
Yunjuan Jia
Abstract
Gasification is a highly potential technology in the energy sector, which involves complex chemical and high-temperature thermal processes. It is primarily a process that alters biomass into a compound of valuable fuel gases and byproducts through a series of chemical reactions like oxidation, reduction, and pyrolysis. The gaseous products so produced are important constituents in energy generation. Solid residues like char and ash are also produced as byproducts. Despite its potential, biomass gasification is a highly complicated process and requires extensive modeling and simulation studies. The Radial Basis Function (RBF) model, which is highly recognized for tracking complex links using historical data, is one of them. On top of that, for further fine-tuning of the projection accuracy, advanced enhancement schemes such as Flying Foxes Optimization (FFO) and Hunger Game Search Optimization (HGSO) are used in this work. From this point of view, the developed model presents substantial advances concerning the optimization of the gasification process, taking into account the large emphasis on sustainability and major untapped opportunities that data-driven approaches create within this key field. A comparative evaluation based on R 2 and RMSE values demonstrates that the RBF coupled with the FFO (RBFF) model significantly outperforms both the conventional RBF and the RBF coupled with HGSO (RBHG) schemes. For CH4 projection, RBFF achieves an R 2 of 0.991 with an RMSE of 0.314, surpassing RBF (R 2 = 0.973, RMSE = 0.543) and RBHG (R 2 = 0.983, RMSE = 0.431). Similarly, for C2H n , RBFF attains the peak R 2 of 0.991 and the lowest RMSE of 0.135, compared to RBF (R 2 = 0.973, RMSE = 0.229) and RBHG (R 2 = 0.981, RMSE = 0.196). These findings stress the impact of hybrid enhancement in adjusting gasification modeling, reinforcing the RBFF model’s effectiveness in advancing gasification technology.
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 “Yanling XU, Bing WU and Yunjuan JIA”. The first draft of the manuscript was written by “Bing WU” and all authors commented on previous versions of the manuscript. All authors have read and approved 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 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 authors do not have permission to share data.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
- Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
- Random Forest model for precise cooling load estimation in optimized and non-optimized form
- Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
- A numerical simulation of nucleate boiling of water on inclined and rough surfaces
- Optimization and modelling of process parameters for single pass plasma arc welded steel using response surface methodology
- Forecasting gasification process results via radial basis function optimization schemes
- Machine learning approaches for predicting syngas production in biomass gasification
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Numerical investigation of superheating secondary flow on performance of steam ejector by considering non-equilibrium condensation in renewable refrigeration cycle
- Analysis of pressure drop, energy requirements, and entropy generation in natural gas pipelines at dense and pseudo-dense phases: a CFD study
- Random Forest model for precise cooling load estimation in optimized and non-optimized form
- Energy recovery from mechanical energy of high-pressure natural gas pipeline: a case study simulation
- A numerical simulation of nucleate boiling of water on inclined and rough surfaces
- Optimization and modelling of process parameters for single pass plasma arc welded steel using response surface methodology
- Forecasting gasification process results via radial basis function optimization schemes
- Machine learning approaches for predicting syngas production in biomass gasification
