Journal of Production Engineering

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Vol. 28 No. 1 (2025)
Original Research Article

Computer-aided energy prediction for selected and blended wood biomass using ultimate and proximate analysis

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  • Anthony Y, Oyerinde,
  • Emmanuel I. Awode,
  • Olufemi S. Bamisaye,
  • Ayowunmi Rita Soji-Adekunle ,
  • Goodhead Godspower,
  • Number Daniel
Anthony Y, Oyerinde
Industrial and Production Engineering Department, The Federal University of Technology, Akure, Ondo State, Nigeria
Emmanuel I. Awode
Mechanical Engineering Department, Air Force Institute of Technology, Kaduna, Kaduna State, Nigeria
Olufemi S. Bamisaye
Mechanical Engineering Department, Faculty of Air Engineering, Air Force Institute of Technology, Kaduna, Nigeria
Ayowunmi Rita Soji-Adekunle
Mechanical Engineering Department, Adeleke University, Ede, Osun State, Nigeria
Goodhead Godspower
Mechanical Engineering Department, Adeleke University, Ede, Osun State, Nigeria
Number Daniel
Mechanical Engineering Department, Adeleke University, Ede, Osun State, Nigeria
DOI: https://doi.org/10.24867/JPE-2025-01-008

Published 2025-06-24

abstract views: 61 // FULL TEXT ARTICLE (PDF): 0


Keywords

  • Wood Biomass,
  • Regression model,
  • Sustainable energy,
  • Calorific value

How to Cite

Oyerinde, Anthony Y, Emmanuel I. Awode, Olufemi S. Bamisaye, Ayowunmi Rita Soji-Adekunle, Goodhead Godspower, and Number Daniel. 2025. “Computer-Aided Energy Prediction for Selected and Blended Wood Biomass Using Ultimate and Proximate Analysis”. Journal of Production Engineering 28 (1):8-18. https://doi.org/10.24867/JPE-2025-01-008.

Copyright (c) 2025 Journal of Production Engineering

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study evaluates the energy potential of wood biomass (sawdust) by employing computer-aided techniques to predict the higher heating value (HHV) through ultimate and proximate analyses. The ultimate analysis focuses on elemental properties, while the proximate analysis examines physical properties. The developed regression model demonstrates a high coefficient of determination (R²) of 99.69% for ultimate analysis, indicating a strong predictive capability. In contrast, the proximate analysis reveals individual correlation coefficients of 85.80% for moisture content, 79.18% for fixed carbon, and 28.10% for volatile matter. To assess the significance of each independent variable in the model, the p-values associated with the coefficients were examined. For the ultimate analysis, all input variables except for sulfur (%S) (p ≈ 0.22) had p-values less than 0.05 at a 95% significance level, indicating their statistical significance. However, in the proximate analysis, only volatile matter exhibited a relatively high p-value (p ≈ 0.12), rendering it statistically insignificant in the model. The elevated p-values for sulfur and volatile matter suggest their minimal impact on HHV predictions in their respective models. The computer program developed for this study automates the prediction process, achieving an accuracy within ±5 MJ/kg between predicted and experimental values across the dataset and is uniformly applicable to all individual models or biomass type, significantly reducing analysis time. The findings of this study contribute to optimizing biomass energy systems, enhancing energy recovery efficiency, and advancing sustainable energy practices.

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