Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
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Chang Tai
and
Shasha Xiong
Abstract
In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses the growing energy demands. However, the effectiveness of biomass gasification is intricately tied to its predictability and efficiency, presenting a substantial challenge in achieving optimal operational parameters for this complex process. It is at this precise juncture that machine learning assumes a pivotal role, initiating a transformative paradigm shift in the approach to biomass gasification. This article delves into the convergence of machine learning and the prediction of biomass gasification and introduces two innovative hybrid models that amalgamate the Support Vector Regression (SVR) algorithm with Coot Optimization Algorithm (COA) and Walrus Optimization Algorithm (WaOA). These models harness nearby biomass data to forecast the elemental compositions of CH4 and C2Hn, thereby enhancing the precision and practicality of biomass gasification predictions, offering potential solutions to the intricate challenges within the domain. The SVWO model (SVR optimized with WaOA) is an effective tool for predicting these elemental compositions. SVWO exhibited outstanding performance with notable R 2 values of 0.992 for CH4 and 0.994 for C2Hn, emphasizing its exceptional accuracy. Additionally, the minimal RMSE values of 0.317 for CH4 and 0.136 for C2Hn underscore the precision of SVWO. This accuracy in SVWO’s predictions affirms its suitability for practical, real-world applications.
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 neccessary due to that the data were collected from the references.
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Author contributions: Authors’ contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Chang TAI and Shasha XIONG”. The first draft of the manuscript was written by “Chang TAI and Shasha XIONG” commented on previous versions of the manuscript. All authors have read and approved the manuscript.
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Competing interests: The authors declare no competing of interests.
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Research funding: No Funding.
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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
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
Articles in the same Issue
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
