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Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents

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  • Akdeniz, Fikret
  • Biçil, Metin
  • Karadede, Yusuf
  • Özbek, Füreya Elif
  • Özdemir, Gültekin

Abstract

The higher heating values (HHVs) of 11 non-wood lignocellulosic materials from Turkey were measured experimentally and calculated incorporating various theoretical models with the values of both lignin and extractive contents. Multiple linear regression (MLR) and real valued genetic algorithm (RVGA) were used to derive the theoretical models. A non-linear RVGA6 model was determined as the best non-linear model considering the experimental results with a regression coefficient of 92% coefficient of determination (R2), 0.301 sum of squared errors (SSE), 0.301 mean squared errors (MSE), 0.548 root mean squared errors (RMSE) and 0.0187 mean absolute percentage error (MAPE) and is proposed as a better alternative for theoretical HHV calculations to the multiple linear modellings such as MLR and RVGA1.

Suggested Citation

  • Akdeniz, Fikret & Biçil, Metin & Karadede, Yusuf & Özbek, Füreya Elif & Özdemir, Gültekin, 2018. "Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents," Energy, Elsevier, vol. 160(C), pages 1047-1054.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:1047-1054
    DOI: 10.1016/j.energy.2018.07.053
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    Cited by:

    1. Bruno Esteves & Umut Sen & Helena Pereira, 2023. "Influence of Chemical Composition on Heating Value of Biomass: A Review and Bibliometric Analysis," Energies, MDPI, vol. 16(10), pages 1-17, May.
    2. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    3. Pegoretti Leite de Souza, Hector Jesus & Muñoz, Fernando & Mendonça, Regis Teixeira & Sáez, Katia & Olave, Rodrigo & Segura, Cristina & de Souza, Daniel P.L. & de Paula Protásio, Thiago & Rodríguez-So, 2021. "Influence of lignin distribution, physicochemical characteristics and microstructure on the quality of biofuel pellets made from four different types of biomass," Renewable Energy, Elsevier, vol. 163(C), pages 1802-1816.
    4. Ren, Yangguang & Lv, Ziqi & Xu, Zhiqiang & Wang, Qun & Wang, Zhe, 2023. "Slurry-ability mathematical modeling of microwave-modified lignite: A comparative analysis of multivariate non-linear regression model and XGBoost algorithm model," Energy, Elsevier, vol. 281(C).
    5. Theodoros N. Kapetanakis & Ioannis O. Vardiambasis & Christos D. Nikolopoulos & Antonios I. Konstantaras & Trinh Kieu Trang & Duy Anh Khuong & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & D, 2021. "Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge," Energies, MDPI, vol. 14(11), pages 1-15, May.
    6. Ding, Yanming & Huang, Biqing & Wu, Chuanbao & He, Qize & Lu, Kaihua, 2019. "Kinetic model and parameters study of lignocellulosic biomass oxidative pyrolysis," Energy, Elsevier, vol. 181(C), pages 11-17.
    7. Ioannis O. Vardiambasis & Theodoros N. Kapetanakis & Christos D. Nikolopoulos & Trinh Kieu Trang & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & Dimitrios Kalderis, 2020. "Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values," Energies, MDPI, vol. 13(17), pages 1-20, September.

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