Method of Biomass Discrimination for Fast Assessment of Calorific Value

Jarosław Gocławski, Ewa Korzeniewska, Joanna Sekulska-Nalewajko, Paweł Kiełbasa and Tomasz Dróżdż
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Jarosław Gocławski: Institute of Applied Computer Science, Lodz University of Technology, 18 Stefanowskiego Street, 90-537 Lodz, Poland
Ewa Korzeniewska: Institute of Electrical Engineering Systems, Lodz University of Technology, 18 Stefanowskiego Street, 90-537 Lodz, Poland
Joanna Sekulska-Nalewajko: Institute of Applied Computer Science, Lodz University of Technology, 18 Stefanowskiego Street, 90-537 Lodz, Poland
Paweł Kiełbasa: Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka Av. 116B, 30-149 Cracow, Poland
Tomasz Dróżdż: Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka Av. 116B, 30-149 Cracow, Poland

Energies, 2022, vol. 15, issue 7, 1-23

Abstract: Crop byproducts are alternatives to nonrenewable energy resources. Burning biomass results in lower emission of undesirable nitrogen and sulfur oxides and contributes no significant greenhouse effect. There is a diverse range of energy-useful biomass, including in terms of calorific value. This article presents a new method of discriminating biomass, and of determining its calorific value. The method involves extracting the selected texture features on the surface of a briquette from a microscopic image and then classifying them using supervised classification methods. The fractal dimension, local binary pattern (LBP), and Haralick features are computed and then classified by linear discrimination analysis ( LDA ). The discrimination results are compared with the results obtained by random forest ( RF ) and deep neural network ( DNN ) type classifiers. This approach is superior in terms of complexity and operating time to other methods such as, for instance, the calorimetric method or analysis of the chemical composition of elements in a sample. In the normal operation mode, our method identifies the calorific value in the time of about 100 s, i.e., 90 times faster than traditional combustion of material samples. In predicting from a single sample image, the overall average accuracy of 95% was achieved for all tested classifiers. The authors’ idea to use ten input images of the same material and then majority voting after classification increases the discrimination system accuracy above 99%.

Keywords: biofuel; biomass; calorific value; image analysis; textural features; random forest; linear discrimination; deep neural network; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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