IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2098-d1135653.html
   My bibliography  Save this article

Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass

Author

Listed:
  • Ivan Brandić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Lato Pezo

    (Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12/V, 11000 Belgrade, Serbia)

  • Nikola Bilandžija

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Anamarija Peter

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Jona Šurić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Neven Voća

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

Abstract

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis “goodness of fit” showed that the ANN model has the best performance in terms of coefficient of determination (R 2 = 0.90) and the lowest level of model error for the parameters X 2 (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field.

Suggested Citation

  • Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2098-:d:1135653
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2098/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2098/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    3. 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.
    4. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    5. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    6. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    7. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    8. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    4. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    5. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    6. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    7. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    8. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    9. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    10. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    11. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    12. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    13. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    14. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    15. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    16. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    17. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    18. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    19. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    20. Mohamed Kais Msakni & Anders Risan & Peter Schütz, 2023. "Using machine learning prediction models for quality control: a case study from the automotive industry," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2098-:d:1135653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.