Property-based biomass feedstock grading using k-Nearest Neighbour technique

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22 Citations (Scopus)


Energy generation from biomass requires a nexus of different sources irrespective of origin. A detailed and scientific understanding of the class to which a biomass resource belongs is therefore highly essential for energy generation. An intelligent classification of biomass resources based on properties offers a high prospect in analytical, operational and strategic decision-making. This study proposes the k-Nearest Neighbour (k-NN) classification model to classify biomass based on their properties. The study scientifically classified 214 biomass dataset obtained from several articles published in reputable journals. Four different values of k (k=1,2,3,4) were experimented for various self normalizing distance functions and their results compared for effectiveness and efficiency in order to determine the optimal model. The k–NN model based on Mahalanobis distance function revealed a great accuracy at k=3 with Root Mean Squared Error (RMSE), Accuracy, Error, Sensitivity, Specificity, False positive rate, Kappa statistics and Computation time (in seconds) of 1.42, 0.703, 0.297, 0.580, 0.953, 0.047, 0.622, and 4.7 respectively. The authors concluded that k–NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that k–NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins.

Original languageEnglish
Article number116346
Publication statusPublished - 1 Jan 2020


  • Biomass classification
  • Energy
  • Mahalanobis distance
  • k-NN classifier

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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