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Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation

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  • Noushabadi, Abolfazl Sajadi
  • Dashti, Amir
  • Ahmadijokani, Farhad
  • Hu, Jinguang
  • Mohammadi, Amir H.

Abstract

To have a sustainable economy and environment, several countries have widely inclined to the utilization of non-fossil fuels like biomass fuels to produce heat and electricity. The advantage of employing biomass for combustion is emerging as a potential renewable energy, which is regarded as a cheap fuel. Chemical constituents or elements are essential properties in biomass applications, which would be costly and labor-intensive to experimentally estimate them. One of the criteria to evaluate the energy of biomass from an economic perspective is the higher heating value (HHV). In the present work, we have applied multilayer perceptron artificial neural network (MLP-ANN), least-squares support vector machine (LSSVM), ant colony-adaptive neuro-fuzzy inference system (ACO-ANFIS), particle swarm optimization- ANFIS (PSO-ANFIS), genetic algorithm-radial basis function (GA-RBF) and new multivariate nonlinear regression (MNR) as accurate correlation methods to estimate HHVs of biomass fuels based on the ultimate analysis. 535 experimental data were gathered from literature and categorized into eight classes of by-products of fruits, agri-wastes, wood chips/tree species, grasses/leaves/fibrous materials, other waste materials, briquettes/charcoals/pellets, cereal and Industrial wastes. In the term of statistical analysis, average absolute relative deviation (AARD) authenticates that MNR and GA-RBF algorithm with %AARD of 3.5 and 3.4 could be used to estimate HHV. In addition, developed models results were compared to the results of 69 recently previously published empirical correlations and it confirms the reliability of our results. Relevency factor shows the impact of biomass elements on HHV and outlier analysis indicates the unreliable experimental data. The results of this study can be used by researchers to design and optimize biomass combustion systems.

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  • 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.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:550-562
    DOI: 10.1016/j.renene.2021.07.003
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    References listed on IDEAS

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    2. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2022. "Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
    3. Leni Maulinda & Husni Husin & Nasrul Arahman & Cut Meurah Rosnelly & Muhammad Syukri & Nurhazanah & Fahrizal Nasution & Ahmadi, 2023. "The Influence of Pyrolysis Time and Temperature on the Composition and Properties of Bio-Oil Prepared from Tanjong Leaves ( Mimusops elengi )," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    4. Bi, Yingxin & Chen, Chunxiang & Huang, Xiaodong & Wang, Haokun & Wei, Guangsheng, 2023. "Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)," Energy, Elsevier, vol. 262(PA).
    5. Mollanoori, Mohammad & Dehghan, Ali Akbar, 2024. "Estimating the higher heating value and chemical exergy of solid, liquid, and natural gas fossil fuels," Energy, Elsevier, vol. 302(C).
    6. Małgorzata Sieradzka & Agata Mlonka-Mędrala & Izabela Kalemba-Rec & Markus Reinmöller & Felix Küster & Wojciech Kalawa & Aneta Magdziarz, 2022. "Evaluation of Physical and Chemical Properties of Residue from Gasification of Biomass Wastes," Energies, MDPI, vol. 15(10), pages 1-19, May.

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