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September 5, 2025E3S Web of ConferencesOpen Access

Multivariate analysis and soft computing-based prediction of energy potential in heterogenous waste streams

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Authors

OAOluwatobi AdelekeTJTien‐Chien Jen

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Overview

Data-driven framework analyzes waste properties to enhance energy recovery strategies, suggesting optimal classification methods.

Key Points

  • Carbon and hydrogen significantly influence energy prediction, contributing to 75-80% of model strength.
  • Random Forest model showed superior predictive performance with RMSE, MAE, MAD values of 0.8606, 0.5945, and 0.3864.
  • Dimensionality reduction captured over 90-95% variance using Principal Component Analysis in waste clustering.
  • Analysis utilizes machine learning and statistical techniques to improve waste-to-energy classification and energy estimation.

Cite This Study

Adeleke et al. (2025) studied this question.

synapsesocial.com/papers/68c23965b210217d6477b1b4https://doi.org/10.1051/e3sconf/202564702001
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Generalized ANN Model for Predicting the Energy Potential of Heterogeneous Waste2025
  2. 2A Comprehensive Study of Machine Learning for Waste-to-Energy Process Modeling and Optimization2025
  3. 3Development of robust machine learning models to estimate hydrochar higher heating value and yield based upon biomass proximate analysis2025
  4. 4Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production2025 · 3 citations
  5. 5Soft computing techniques for the assessment of energy content from waste: A mini review2025