IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v324y2022ics0306261922009862.html
   My bibliography  Save this article

A comparative analysis of biomass torrefaction severity index prediction from machine learning

Author

Listed:
  • Chen, Wei-Hsin
  • Aniza, Ria
  • Arpia, Arjay A.
  • Lo, Hsiu-Ju
  • Hoang, Anh Tuan
  • Goodarzi, Vahabodin
  • Gao, Jianbing

Abstract

Machine learning (ML) is one type of artificial intelligence (AI) commonly used for computer programming. Multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) are two common and popular tools in AI that allow the user to analyze the pattern of complex data. The torrefaction severity index (TSI) is an index to define torrefied biomass quality at different torrefaction conditions. In this study, MARS and ANN models are applied to predict TSI. The considered input parameters in predictions using MARS and ANN approaches comprise feedstock type, temperature, and duration. The MARS model indicates that temperature is the most influential factor on TSI, followed by duration and feedstock type. In contrast, the ANN model reveals that the feedstock type is a dominant factor, and temperature and duration are not important. The performance of the ANN model is evaluated in three different combinations of numbers of hidden layers and neurons. It shows 2 hidden layers along with 85 neurons giving the best performance. The highest R2 values in MARS and ANN are 0.9851 and 0.9784, respectively. The relative root means square error analysis shows that both MARS and ANN have good fit quality with the relative errors of 1.49% and 2.16%, respectively. Overall, the comparison reflects that MARS is a more suitable model for predicting solid biofuel’s TSI. The general observation suggests that the ANN lacks sensitivity to the input parameter. Nevertheless, ANN performance may be improved by adjusting the number of hidden layers and neurons.

Suggested Citation

  • Chen, Wei-Hsin & Aniza, Ria & Arpia, Arjay A. & Lo, Hsiu-Ju & Hoang, Anh Tuan & Goodarzi, Vahabodin & Gao, Jianbing, 2022. "A comparative analysis of biomass torrefaction severity index prediction from machine learning," Applied Energy, Elsevier, vol. 324(C).
  • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922009862
    DOI: 10.1016/j.apenergy.2022.119689
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922009862
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.119689?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Jiao & Feng, Shuo & Zhang, Zhikun & Wang, Zhuozhi & Kong, Wenwen & Yuan, Peng & Shen, Boxiong & Mu, Lan, 2022. "Effect of torrefaction pretreatment on the combustion characteristics of the biodried products derived from municipal organic wastes," Energy, Elsevier, vol. 239(PD).
    2. Feng, Yipeng & Qiu, Keying & Zhang, Zhiping & Li, Chong & Rahman, Md. Maksudur & Cai, Junmeng, 2022. "Distributed activation energy model for lignocellulosic biomass torrefaction kinetics with combined heating program," Energy, Elsevier, vol. 239(PC).
    3. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    4. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    5. Zhang, Congyu & Ho, Shih-Hsin & Chen, Wei-Hsin & Xie, Youping & Liu, Zhenquan & Chang, Jo-Shu, 2018. "Torrefaction performance and energy usage of biomass wastes and their correlations with torrefaction severity index," Applied Energy, Elsevier, vol. 220(C), pages 598-604.
    6. Chen, Wei-Hsin & Kuo, Po-Chih, 2011. "Torrefaction and co-torrefaction characterization of hemicellulose, cellulose and lignin as well as torrefaction of some basic constituents in biomass," Energy, Elsevier, vol. 36(2), pages 803-811.
    7. Chen, Wei-Hsin & Huang, Ming-Yueh & Chang, Jo-Shu & Chen, Chun-Yen, 2015. "Torrefaction operation and optimization of microalga residue for energy densification and utilization," Applied Energy, Elsevier, vol. 154(C), pages 622-630.
    8. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    9. Aniza, Ria & Chen, Wei-Hsin & Lin, Yu-Ying & Tran, Khanh-Quang & Chang, Jo-Shu & Lam, Su Shiung & Park, Young-Kwon & Kwon, Eilhann E. & Tabatabaei, Meisam, 2021. "Independent parallel pyrolysis kinetics of extracted proteins and lipids as well as model carbohydrates in microalgae," Applied Energy, Elsevier, vol. 300(C).
    10. Motta, Ingrid Lopes & Miranda, Nahieh Toscano & Maciel Filho, Rubens & Wolf Maciel, Maria Regina, 2018. "Biomass gasification in fluidized beds: A review of biomass moisture content and operating pressure effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 998-1023.
    11. Gan, Yong Yang & Ong, Hwai Chyuan & Ling, Tau Chuan & Chen, Wei-Hsin & Chong, Cheng Tung, 2019. "Torrefaction of de-oiled Jatropha seed kernel biomass for solid fuel production," Energy, Elsevier, vol. 170(C), pages 367-374.
    12. Chen, Wei-Hsin & Lin, Yu-Ying & Liu, Hsuah-Cheng & Chen, Teng-Chien & Hung, Chun-Hung & Chen, Chi-Hui & Ong, Hwai Chyuan, 2019. "A comprehensive analysis of food waste derived liquefaction bio-oil properties for industrial application," Applied Energy, Elsevier, vol. 237(C), pages 283-291.
    13. Ubando, Aristotle T. & Rivera, Diana Rose T. & Chen, Wei-Hsin & Culaba, Alvin B., 2020. "Life cycle assessment of torrefied microalgal biomass using torrefaction severity index with the consideration of up-scaling production," Renewable Energy, Elsevier, vol. 162(C), pages 1113-1124.
    14. Abdullah, Bawadi & Syed Muhammad, Syed Anuar Faua’ad & Shokravi, Zahra & Ismail, Shahrul & Kassim, Khairul Anuar & Mahmood, Azmi Nik & Aziz, Md Maniruzzaman A., 2019. "Fourth generation biofuel: A review on risks and mitigation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 37-50.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manish Meena & Hrishikesh Kumar & Nitin Dutt Chaturvedi & Andrey A. Kovalev & Vadim Bolshev & Dmitriy A. Kovalev & Prakash Kumar Sarangi & Aakash Chawade & Manish Singh Rajput & Vivekanand Vivekanand , 2023. "Biomass Gasification and Applied Intelligent Retrieval in Modeling," Energies, MDPI, vol. 16(18), pages 1-21, September.
    2. Mohamad Aziz, Nur Atiqah & Mohamed, Hassan & Kania, Dina & Ong, Hwai Chyuan & Zainal, Bidattul Syirat & Junoh, Hazlina & Ker, Pin Jern & Silitonga, A.S., 2024. "Bioenergy production by integrated microwave-assisted torrefaction and pyrolysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Zhang, Yao & Salem, Mohamed & Elmasry, Yasser & Hoang, Anh Tuan & Galal, Ahmed M. & Pham Nguyen, Dang Khoa & Wae-hayee, Makatar, 2022. "Triple-objective optimization and electrochemical/technical/environmental study of biomass gasification process for a novel high-temperature fuel cell/electrolyzer/desalination scheme," Renewable Energy, Elsevier, vol. 201(P1), pages 379-399.
    4. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.
    5. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    6. Antonios Nazos & Dorothea Politi & Georgios Giakoumakis & Dimitrios Sidiras, 2022. "Simulation and Optimization of Lignocellulosic Biomass Wet- and Dry-Torrefaction Process for Energy, Fuels and Materials Production: A Review," Energies, MDPI, vol. 15(23), pages 1-35, November.
    7. Zhang, Yonghong & Li, Shouwei & Li, Jingwei & Tang, Xiaoyu, 2022. "A time power-based grey model with Caputo fractional derivative and its application to the prediction of renewable energy consumption," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

    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. Ong, Hwai Chyuan & Yu, Kai Ling & Chen, Wei-Hsin & Pillejera, Ma Katreena & Bi, Xiaotao & Tran, Khanh-Quang & Pétrissans, Anelie & Pétrissans, Mathieu, 2021. "Variation of lignocellulosic biomass structure from torrefaction: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    2. Oh, Kwang Cheol & Park, Sun Young & Kim, Seok Jun & Choi, Yun Sung & Lee, Chung Geon & Cho, La Hoon & Kim, Dae Hyun, 2019. "Development and validation of mass reduction model to optimize torrefaction for agricultural byproduct biomass," Renewable Energy, Elsevier, vol. 139(C), pages 988-999.
    3. Arriola, Emmanuel & Chen, Wei-Hsin & Chih, Yi-Kai & De Luna, Mark Daniel & Show, Pau Loke, 2020. "Impact of post-torrefaction process on biochar formation from wood pellets and self-heating phenomena for production safety," Energy, Elsevier, vol. 207(C).
    4. da Silva, Jean Constantino Gomes & Pereira, Jefferson Leque Claudio & Andersen, Silvia Layara Floriani & Moreira, Regina de Fatima Peralta Muniz & José, Humberto Jorge, 2020. "Torrefaction of ponkan peel waste in tubular fixed-bed reactor: In-depth bioenergetic evaluation of torrefaction products," Energy, Elsevier, vol. 210(C).
    5. Ong, Hwai Chyuan & Chen, Wei-Hsin & Farooq, Abid & Gan, Yong Yang & Lee, Keat Teong & Ashokkumar, Veeramuthu, 2019. "Catalytic thermochemical conversion of biomass for biofuel production: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Zhang, Shuping & Su, Yinhai & Xu, Dan & Zhu, Shuguang & Zhang, Houlei & Liu, Xinzhi, 2018. "Effects of torrefaction and organic-acid leaching pretreatment on the pyrolysis behavior of rice husk," Energy, Elsevier, vol. 149(C), pages 804-813.
    7. Zhang, Congyu & Ho, Shih-Hsin & Chen, Wei-Hsin & Wang, Rupeng, 2021. "Comparative indexes, fuel characterization and thermogravimetric- Fourier transform infrared spectrometer-mass spectrogram (TG-FTIR-MS) analysis of microalga Nannochloropsis Oceanica under oxidative a," Energy, Elsevier, vol. 230(C).
    8. Zhang, Congyu & Chen, Wei-Hsin & Ho, Shih-Hsin, 2022. "Elemental loss, enrichment, transformation and life cycle assessment of torrefied corncob," Energy, Elsevier, vol. 242(C).
    9. Maja Ivanovski & Aleksandra Petrovič & Darko Goričanec & Danijela Urbancl & Marjana Simonič, 2023. "Exploring the Properties of the Torrefaction Process and Its Prospective in Treating Lignocellulosic Material," Energies, MDPI, vol. 16(18), pages 1-20, September.
    10. Gan, Yong Yang & Ong, Hwai Chyuan & Ling, Tau Chuan & Chen, Wei-Hsin & Chong, Cheng Tung, 2019. "Torrefaction of de-oiled Jatropha seed kernel biomass for solid fuel production," Energy, Elsevier, vol. 170(C), pages 367-374.
    11. Dimitrios K. Sidiras & Antonios G. Nazos & Georgios E. Giakoumakis & Dorothea V. Politi, 2020. "Simulating the Effect of Torrefaction on the Heating Value of Barley Straw," Energies, MDPI, vol. 13(3), pages 1-15, February.
    12. Chen, Wei-Hsin & Lin, Yu-Ying & Liu, Hsuan-Cheng & Baroutian, Saeid, 2020. "Optimization of food waste hydrothermal liquefaction by a two-step process in association with a double analysis," Energy, Elsevier, vol. 199(C).
    13. Devaraja, Udya Madhavi Aravindi & Senadheera, Sachini Supunsala & Gunarathne, Duleeka Sandamali, 2022. "Torrefaction severity and performance of Rubberwood and Gliricidia," Renewable Energy, Elsevier, vol. 195(C), pages 1341-1353.
    14. Zhang, Congyu & Yang, Wu & Chen, Wei-Hsin & Ho, Shih-Hsin & Pétrissans, Anelie & Pétrissans, Mathieu, 2022. "Effect of torrefaction on the structure and reactivity of rice straw as well as life cycle assessment of torrefaction process," Energy, Elsevier, vol. 240(C).
    15. Singh, Rishikesh Kumar & Sarkar, Arnab & Chakraborty, Jyoti Prasad, 2020. "Effect of torrefaction on the physicochemical properties of eucalyptus derived biofuels: estimation of kinetic parameters and optimizing torrefaction using response surface methodology (RSM)," Energy, Elsevier, vol. 198(C).
    16. Singh, Rishikesh Kumar & Chakraborty, Jyoti Prasad & Sarkar, Arnab, 2020. "Optimizing the torrefaction of pigeon pea stalk (cajanus cajan) using response surface methodology (RSM) and characterization of solid, liquid and gaseous products," Renewable Energy, Elsevier, vol. 155(C), pages 677-690.
    17. Silveira, Edgar A. & Macedo, Lucélia A. & Rousset, Patrick & Candelier, Kevin & Galvão, Luiz Gustavo O. & Chaves, Bruno S. & Commandré, Jean-Michel, 2022. "A potassium responsive numerical path to model catalytic torrefaction kinetics," Energy, Elsevier, vol. 239(PB).
    18. Catarina Viegas & Catarina Nobre & Ricardo Correia & Luísa Gouveia & Margarida Gonçalves, 2021. "Optimization of Biochar Production by Co-Torrefaction of Microalgae and Lignocellulosic Biomass Using Response Surface Methodology," Energies, MDPI, vol. 14(21), pages 1-23, November.
    19. Zhang, Congyu & Ho, Shih-Hsin & Chen, Wei-Hsin & Fu, Yujie & Chang, Jo-Shu & Bi, Xiaotao, 2019. "Oxidative torrefaction of biomass nutshells: Evaluations of energy efficiency as well as biochar transportation and storage," Applied Energy, Elsevier, vol. 235(C), pages 428-441.
    20. Antonios Nazos & Panagiotis Grammelis & Elias Sakellis & Dimitrios Sidiras, 2020. "Acid-Catalyzed Wet Torrefaction for Enhancing the Heating Value of Barley Straw," Energies, MDPI, vol. 13(7), pages 1-16, April.

    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:eee:appene:v:324:y:2022:i:c:s0306261922009862. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.