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Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

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Abstract

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (>0.95) magnitudes of the coefficient of correlation and low (<4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

CC:

Coefficient of correlation

EBP:

Error back propagation

GA:

Genetic algorithms

GP:

Genetic programming

HHV:

Higher heating value

MAPE:

Mean absolute percentage error

MIMO:

Multiple input–multiple output

MISO:

Multiple input–single output

MLP:

Multilayer perceptron neural network

RMSE:

Root mean squared error

SVR:

Support vector regression

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Acknowledgments

This work was partly supported by the Council of Scientific and Industrial Research (Network project: Tapcoal), Government of India, New Delhi.

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Author notes

  1. V. Elangovan

    Present address: Chemical Engg. Department, National Institute of Technology (NIT), Tiruchirapalli, India

Authors and Affiliations

  1. Artificial Intelligence Systems Group, Chemical Engineering & Process Development Division, National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune, 411 008, India

    S. B. Ghugare, S. Tiwary, V. Elangovan & S. S. Tambe

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  1. S. B. Ghugare
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  2. S. Tiwary
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  3. V. Elangovan
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  4. S. S. Tambe
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Correspondence to S. S. Tambe.

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Ghugare, S.B., Tiwary, S., Elangovan, V. et al. Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms. Bioenerg. Res. 7, 681–692 (2014). https://doi.org/10.1007/s12155-013-9393-5

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