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RETRACTED ARTICLE: Management of higher heating value sensitivity of biomass by hybrid learning technique

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This article was retracted on 18 March 2023

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Abstract

Recently, biomass sources are important for energy applications. Therefore, there is need for analyzing the biomass model based on different components such as carbon, ash, and moisture content. Since the biomass modeling could be very challenging task for conventional mathematical, it is suitable to apply soft computing models which could overcome the nonlinearities of the process. The main attempt in the study was to develop a soft computing model for the prediction of the higher heating values of biomass based on the proximate analysis. Adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology. According to the prediction accuracy of the higher heating value of the biomass, the inputs’ influence was determined on the higher heating value. According to the obtained results, fixed carbon has a correlation coefficient of 0.7644, the volatile matter has a correlation coefficient of 0.7225, and ash has a correlation coefficient of 0.9317. Therefore, the ash percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the volatile matter has the smallest relevance on the higher heating value of the biomass.

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Authors and Affiliations

  1. Faculty of Business and Law, MB University, Belgrade, Serbia

    Nadja Lakovic

  2. Institute of Engineering and Technology, Department of Hydraulics and Hydraulic and Pneumatic Systems, South Ural State University, Lenin Prospect 76, Chelyabinsk, Russian Federation, 454080

    Afrasyab Khan

  3. Faculty of Economics, University of Kragujevac, Licej Kneževine Srbije 3, Kragujevac, Serbia

    Biljana Petković

  4. Pedagogical Faculty in Vranje, University of Niš, Partizanska 14, Vranje, Serbia

    Dalibor Petkovic

  5. Institute of agricultural economics, Belgrade, Serbia

    Boris Kuzman

  6. Faculty of Natural Sciences and Mathematics, Department for Mathematics, University of Tuzla, Tuzla, Bosnia and Herzegovina

    Sead Resic

  7. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Kittisak Jermsittiparsert

  8. Faculty of Humanities and Social Sciences, Duy Tan University, Da Nang, 550000, Vietnam

    Kittisak Jermsittiparsert

  9. MBA School, Henan University of Economics and Law, Zhengzhou, 450046, Henan, China

    Kittisak Jermsittiparsert

  10. Division of Computational Physics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sikander Azam

  11. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sikander Azam

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  1. Nadja Lakovic
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  2. Afrasyab Khan
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Correspondence to Kittisak Jermsittiparsert.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s13399-023-04084-1

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Lakovic, N., Khan, A., Petković, B. et al. RETRACTED ARTICLE: Management of higher heating value sensitivity of biomass by hybrid learning technique. Biomass Conv. Bioref. 13, 3029–3036 (2023). https://doi.org/10.1007/s13399-020-01223-w

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