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<p>Biomass gasification is essential for sustainable energy. This study examines how different input and output factors in the gasification process interact using statistical methods like Principal Component Analysis (PCA) and regression modeling. The dataset, collected from literature is used to observe the complex interaction between gasification factors, resulting in prediction equations that estimates output variables as a function of input variables. The biomass gasification process contains independent variables (input) that can vary the dependent variables (output). The data set involved the values of independent variables, viz., proximate analysis, ultimate analysis, higher heating values (HHV), gasification conditions (equivalence ratio (ER), gasification temperature) and their effect on the dependent variables, viz., output gas vol% and gas Lower Heating Value (LHV)). Two PC analyses were performed—one including both independent and dependent variables to capture overall relationships, and another considering only the independent variables to identify the most influential factors affecting gasification outcomes. 6 principal components explained 80.32% of the data variation when considering all the variables, while four principal components accounted for 74.10% of the variation in only the independent variables. The 4 PC generated for independent variables were regressed with the dependent variables to obtain the final regression equation. The model showed strong accuracy, with R<sup>2</sup> values of 0.94 for CO, 0.92 for CO<sub>2</sub>, 0.69 for CH<sub>4</sub>, 0.94 for H<sub>2</sub>, and 0.94 for LHV. The PCA and regression models can be effectively utilized to optimize biomass gasification by identifying key process parameters. By adjusting influencing factors, gas composition and efficiency can be improved for better energy output. The findings can enhance gasification system conceptual design and operation in both industry and research, supporting the development of full-scale systems for more efficient, sustainable energy production. </p> Graphical abstract <p></p>

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Insights into biomass gasification: a statistical analysis

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Deepanshu Awasthi,
  • Arghya Datta,
  • Amrit Pal Toor,
  • Nikhil Gakkhar,
  • Tapas Kumar Patra

摘要

Biomass gasification is essential for sustainable energy. This study examines how different input and output factors in the gasification process interact using statistical methods like Principal Component Analysis (PCA) and regression modeling. The dataset, collected from literature is used to observe the complex interaction between gasification factors, resulting in prediction equations that estimates output variables as a function of input variables. The biomass gasification process contains independent variables (input) that can vary the dependent variables (output). The data set involved the values of independent variables, viz., proximate analysis, ultimate analysis, higher heating values (HHV), gasification conditions (equivalence ratio (ER), gasification temperature) and their effect on the dependent variables, viz., output gas vol% and gas Lower Heating Value (LHV)). Two PC analyses were performed—one including both independent and dependent variables to capture overall relationships, and another considering only the independent variables to identify the most influential factors affecting gasification outcomes. 6 principal components explained 80.32% of the data variation when considering all the variables, while four principal components accounted for 74.10% of the variation in only the independent variables. The 4 PC generated for independent variables were regressed with the dependent variables to obtain the final regression equation. The model showed strong accuracy, with R2 values of 0.94 for CO, 0.92 for CO2, 0.69 for CH4, 0.94 for H2, and 0.94 for LHV. The PCA and regression models can be effectively utilized to optimize biomass gasification by identifying key process parameters. By adjusting influencing factors, gas composition and efficiency can be improved for better energy output. The findings can enhance gasification system conceptual design and operation in both industry and research, supporting the development of full-scale systems for more efficient, sustainable energy production.

Graphical abstract