Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis

  • Suhas B. Ghugare
  • Shishir Tiwary
  • Sanjeev S. TambeEmail author
Original Article


Biomass is a renewable and sustainable source of “green” energy. The elemental composition comprising carbon (C), hydrogen (H) and oxygen (O) as major components, is an important measure of the biomass fuel’s energy content. Its knowledge is also valuable in: (a) computing material balance in a biomass-based process, (b) designing and operating biomass utilizing efficient and clean combustors, gasifiers and boilers, (c) fixing the quantity of oxidants required for biomass combustion/gasification, and (d) determining the volume and composition of the combustion/gasification gases. Obtaining the elemental composition of a biomass fuel via ultimate analysis is an expensive and time-consuming task. In comparison, proximate analysis that determines fixed carbon, ash, volatile matter and moisture content is a cruder characterization of the fuel and easier to perform. Thus, there exists a need for models possessing high accuracies for predicting the elemental composition of a solid biomass fuel from its proximate analysis constituents. Accordingly, this study utilizes three computational intelligence (CI) formalisms, namely, genetic programming, artificial neural networks and support vector regression, for developing nonlinear models for the prediction of C, H and O fractions of solid biomass fuels. A large database of 830 biomasses has been used in the stated model development. A comparison of the prediction accuracy and generalization performance of the nine CI-based models (three each for C, H and O) with that of the currently available linear models indicates that the CI-based models have consistently and significantly outperformed their linear counterparts. The models developed in this study have proved to be the best models for the prediction of elemental composition of solid biomass fuels from their proximate analyses.


Biomass fuels Elemental composition Ultimate analysis Proximate analysis Computational intelligence 



This study is partly supported by the Council of Scientific and Industrial Research (CSIR), Government of India, New Delhi, under Network project (TAPCOAL).

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

13198_2014_324_MOESM1_ESM.doc (983 kb)
Supplementary material 1 (DOC 983 kb)


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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014

Authors and Affiliations

  • Suhas B. Ghugare
    • 1
  • Shishir Tiwary
    • 1
    • 2
  • Sanjeev S. Tambe
    • 1
    Email author
  1. 1.Chemical Engineering and Process Development DivisionCSIR-National Chemical LaboratoryPuneIndia
  2. 2.CSIR-Central Institute of Mining and Fuel Research (CIMFR)DhanbadIndia

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