Machine learning approaches for predicting syngas production in biomass gasification
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Zhe Zhang
Abstract
Fossil fuel dependence causes environmental and resource issues, intensified by climate change and population growth. Renewable sources like solar, wind, and biomass are rising. Biomass contributes 10–14 % of global energy, with gasification offering stable output and useful byproducts. However, efficiency and environmental concerns challenge its economic viability, prompting the need for predictive models under diverse conditions. This study introduces a novel hybrid modeling approach that integrates Naive Bayes (NB) with two advanced metaheuristic optimization algorithms, Jellyfish Search Optimizer (JSO) and Cheetah Optimizer (CO), to enhance the prediction accuracy of elemental compositions of nitrogen and hydrogen from proximate biomass data. The proposed hybrid schemes indicate drastically improved estimation accuracy, and among these, NBCO, i.e., the Naive Bayes + Cheetah Optimizer hybrid, was the most effective. NBCO achieved the RMSE values of 1.472 and 1.955 for nitrogen and hydrogen, validating its better prediction ability. By utilizing the synergistic properties of NB with JSO and CO, this work presents a sound predictive model complementing biomass gasification modeling with a viable tool for optimizing renewable energy processes. With the enhanced accuracy of prediction of elemental composition, the proposed schemes enable better control of biomass gasification processes with increased efficiency in energy production, reduced emissions, and decreased operation costs. Accuracy in determining nitrogen and hydrogen compositions optimizes gasifier efficiency, enabling cleaner, more cost-effective energy production. The model provides a feasible solution for businesses and policymakers seeking to maximize the potential of biomass as a renewable energy source while minimizing environmental and economic problems.
Funding source: . Fund Research on Low Carbon Technology Innovation and Green Development of Changsha’s Manufacturing Industry under the Dual Carbon Goal
Award Identifier / Grant number: kh2302007
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 “Zhe Zhang and Zhigao Chen”. Also, the first draft of the manuscript was written by Zhe Zhang. Zhigao Chen 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: Project of the Changsha Soft Science Research Program provided funding for this study. Fund “Research on Low Carbon Technology Innovation and Green Development of Changsha’s Manufacturing Industry under the Dual Carbon Goal” (kh2302007).
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Data availability: The authors do not have permissions 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
