Implementing a radial basis function model to anticipate the outcomes of the gasification
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Hongfang Cui
and
Hongyan Cui
Abstract
Hydrogen (H₂) and nitrogen (N₂) are both critical components in gasification processes, making efficient conversion of carbonaceous feedstocks into valuable gases with reduced environmental impact indispensable. This study demonstrates a state-of-the-art approach to predictive modeling for these quantities at high accuracy while allowing cost-effective solutions under a variety of operational conditions, enhancing safety, and enabling data-driven optimization. This work develops a new framework that incorporates the radial basis function model with two state-of-the-art optimization algorithms, namely the Zebra Optimization Algorithm (ZOA) and Flow Direction Algorithm (FDA), to enhance the predictive accuracy of gasification processes. This is a new frontier in optimizing the sustainable conversion of carbonaceous feedstocks, demonstrating the potential of data-driven methodologies in process efficiency and environmental sustainability. The RBFD model resulted in outstanding anticipation capability for H2, reaching an exceptional R2 value of 0.997 during the whole period of testing. On the other hand, the RBZO framework proved to be the strongest predictor for N2 anticipation, presenting an outstanding R2 of 0.994 during the testing and validation phases. The RBFD and RBZO frameworks showed significantly higher productivity compared to the conventional RBF model, as evidenced by accuracy metrics like MSE, RMSE, and WAPE.
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 “Hongfang CUI, Ning SU and Hongyan CUI”. The first draft of the manuscript was written by Ning SU 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 ChatGPT by OpenAI and Grammarly in order to assist with language refinement and ensure clarity and coherence in the manuscript, and perform grammar and spell checks. After using these tools/services, the authors reviewed and edited the content as needed 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 permissions to share data.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Cogeneration system’s energy performance improvement by using P-graph and advanced process control
- Numerical simulation of R134a evaporation in a cold water production system
- Implementing a radial basis function model to anticipate the outcomes of the gasification
- Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
- Evaluating the therapeutic potential of 4-hydroxyflavanes diastereomers derivatives against (MetAP2) for anti-cancer therapy: a molecular docking study
- Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach
- Natural Gas and hydrogen blending: a perspective on numerical modeling and CFD analysis for transient and steady-state scenarios
- Simulation and optimization of Venturi type bubble generator to improve cavitation
Articles in the same Issue
- Frontmatter
- Research Articles
- Cogeneration system’s energy performance improvement by using P-graph and advanced process control
- Numerical simulation of R134a evaporation in a cold water production system
- Implementing a radial basis function model to anticipate the outcomes of the gasification
- Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
- Evaluating the therapeutic potential of 4-hydroxyflavanes diastereomers derivatives against (MetAP2) for anti-cancer therapy: a molecular docking study
- Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach
- Natural Gas and hydrogen blending: a perspective on numerical modeling and CFD analysis for transient and steady-state scenarios
- Simulation and optimization of Venturi type bubble generator to improve cavitation
