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Prediction of higher heating value of biochars using proximate analysis by artificial neural network

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Abstract

The biochars obtained from the pyrolysis of biomass at different conditions have the potential to be used as biofuels. Thus, as a critical fuel property, the higher heating value (HHV) of biochars must be determined to decide on their application area. However, oxygen bomb calorimeters that are employed for HHV determination are expensive. Also, analysis is time-consuming, needs specialists, and can suffer from experimental errors. Although some model equations are available for solid fuels (biomass, coal, etc.) to calculate HHV, biochar has different properties, and a new model is required. This study aims to form an artificial neural network (ANN) model in order to estimate HHV of biochars by using simple proximate analysis data of 129 different biochars. The experimental and the predicted model results showed good agreement that the ANN model presented the highest regression coefficient of 0.9651 and the lowest mean absolute deviation of 0.5569 among all models previously reported in the literature.

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Code availability

With software application or custom code

Abbreviations

A:

Ash

ANNs:

Artificial neural networks

FC:

Fixed carbon

HHV:

Higher heating value

MAD:

Median absolute deviation

MAPE:

Mean absolute percentage error

MSE:

Mean square error

RMSE:

Root mean square error

VM:

Volatile matter

b 1, i :

Bias of a neuron N in the hidden layer

IW M, N :

Weight of an input M on a hidden N

N train :

Number of training samples

wt%:

Weight percent

W N, Z :

Weight of hidden neuron N on an output neuron Z

y Act, i :

Actual values for the outcome

y prd, I :

Predicted values for the outcome

y m :

Mean of actual values for the outcome

Z(n):

Calculated values of the output neurons

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Authors and Affiliations

  1. Faculty of Engineering, Chemical Engineering Department, Ondokuz Mayıs University, Kurupelit, 55139, Samsun, Turkey

    Gülce Çakman & Selim Ceylan

  2. Faculty of Engineering, Chemical Engineering Department, Tikrit University, Tikrit, 34001, Iraq

    Saba Gheni

Authors
  1. Gülce Çakman
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  2. Saba Gheni
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  3. Selim Ceylan
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Contributions

Gülce Çakman: investigation, writing—original draft; Saba Gheni: writing–reviewing and editing; Selim Ceylan: supervision, conceptualization, writing–reviewing, and editing.

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Correspondence to Selim Ceylan.

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Çakman, G., Gheni, S. & Ceylan, S. Prediction of higher heating value of biochars using proximate analysis by artificial neural network. Biomass Conv. Bioref. 14, 5989–5997 (2024). https://doi.org/10.1007/s13399-021-01358-4

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