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Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS)

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  • Hosseinpour, Soleiman
  • Aghbashlo, Mortaza
  • Tabatabaei, Meisam
  • Mehrpooya, Mehdi

Abstract

The higher heating value (HHV) of biomass fuels is a crucial factor in the techno-economic analysis and subsequent development of bioenergy projects. In this study, iterative neural network-adapted partial least squares (INNPLS) was applied to estimate the HHV of biomass fuels as a function of fixed carbon (FC), volatile matters (VM), and ash content. The ANN paradigm was used to correlate the inputs and the outputs of PLS score vectors thorough iterative training procedure. The prediction capability of the proposed model was compared with those of the classical PLS, the coupled principle component analysis and ANN paradigm (PCA-ANN), and the neural network-adapted partial least squares (NNPLS). The presented models were developed, trained, and tested using 350 data points obtained from the published literature. According to the results obtained, the INNPLS showed an excellent capability to model the HHV of biomass fuels over the other methods. This approach was then embedded into a simple and user-friendly software for estimating the HHV of biomass fuels on the basis of their proximate data. The developed software can be utilized for reliable and accurate estimation of biomass HHV based on only three input parameters as an alternative to the lengthy and costly laboratorial measurements.

Suggested Citation

  • Hosseinpour, Soleiman & Aghbashlo, Mortaza & Tabatabaei, Meisam & Mehrpooya, Mehdi, 2017. "Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS)," Energy, Elsevier, vol. 138(C), pages 473-479.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:473-479
    DOI: 10.1016/j.energy.2017.07.075
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    References listed on IDEAS

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    1. Hajjari, Masoumeh & Tabatabaei, Meisam & Aghbashlo, Mortaza & Ghanavati, Hossein, 2017. "A review on the prospects of sustainable biodiesel production: A global scenario with an emphasis on waste-oil biodiesel utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 445-464.
    2. Aghbashlo, Mortaza & Tabatabaei, Meisam & Karimi, Keikhosro, 2016. "Exergy-based sustainability assessment of ethanol production via Mucor indicus from fructose, glucose, sucrose, and molasses," Energy, Elsevier, vol. 98(C), pages 240-252.
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    1. Xing, Jiangkuan & Wang, Haiou & Luo, Kun & Wang, Shuai & Bai, Yun & Fan, Jianren, 2019. "Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)," Renewable Energy, Elsevier, vol. 136(C), pages 104-114.
    2. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Ahmadijokani, Farhad & Hu, Jinguang & Mohammadi, Amir H., 2021. "Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation," Renewable Energy, Elsevier, vol. 179(C), pages 550-562.
    3. Łukasz Sobol & Karol Wolski & Adam Radkowski & Elżbieta Piwowarczyk & Maciej Jurkowski & Henryk Bujak & Arkadiusz Dyjakon, 2022. "Determination of Energy Parameters and Their Variability between Varieties of Fodder and Turf Grasses," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    4. Aghbashlo, Mortaza & Mandegari, Mohsen & Tabatabaei, Meisam & Farzad, Somayeh & Mojarab Soufiyan, Mohamad & Görgens, Johann F., 2018. "Exergy analysis of a lignocellulosic-based biorefinery annexed to a sugarcane mill for simultaneous lactic acid and electricity production," Energy, Elsevier, vol. 149(C), pages 623-638.
    5. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    6. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    7. El Hage, Hicham & Herez, Amal & Ramadan, Mohamad & Bazzi, Hassan & Khaled, Mahmoud, 2018. "An investigation on solar drying: A review with economic and environmental assessment," Energy, Elsevier, vol. 157(C), pages 815-829.

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