Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
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Feng Du
Abstract
This study pioneers a method for forecasting hydrogen and nitrogen quantities in gasification processes, which is crucial for transforming carbon-based materials into valuable gases with minimal environmental impact. It addresses the pressing need for precise and economical solutions in gasification by streamlining estimation procedures across various operational conditions. Anchored in historical data, the K-nearest neighbour’s (KNN) model forms the core of this method, adeptly capturing intricate relationships between input variables and gas production results. What sets this research apart is the integration of advanced optimization techniques, Beluga whale optimization (BWO), and Northern Goshawk Optimization (NGO), further refining the accuracy of the KNN model’s predictions. This work signifies a substantial stride in optimizing gasification processes, offering a pathway towards more efficient and sustainable conversion of carbon-based materials, showcasing the potential of data-driven sustainability in this domain, and ultimately diminishing the environmental impact of these operations. Results highlight the outstanding predictive performance of the KNNG model, achieving an impressive R 2 value of 0.995 and 0.994 during training. Notably, both the KNNG and KNBW models outperformed the basic KNN model in forecasting gasification outputs, underscoring the reliability and effectiveness of these optimized models in predicting these processes.
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 “Feng Du”.
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Competing interests: The author declare no competing of interests.
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Research funding: No funding.
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Data availability: The author 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
