IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v151y2020icp463-474.html
   My bibliography  Save this article

Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models

Author

Listed:
  • Wen, Shaoting
  • Buyukada, Musa
  • Evrendilek, Fatih
  • Liu, Jingyong

Abstract

Bioenergy generation from biomass waste through co-combustion/pyrolysis fulfills simultaneously multiple objectives of reductions in fossil fuel use, greenhouse gas emission, and solid waste stream. This experimental study aimed to quantify the multiple co-combustion/pyrolysis responses of textile dyeing sludge (TDS) and incense sticks (IS) as a function of blend ratio (BR), heating rate (HR), atmosphere type (Atm), and temperature (Temp). Joint optimizations, and predictor importance, sensitivity, uncertainty and interaction analyses were conducted using data-driven models for the responses of remaining mass (RM), derivative thermogravimetry (DTG), and differential scanning calorimetry (DSC). The data-driven models compared in this study were Box Behnken design (BBD)-based regression models, general linear models (GLM), and the six full models with all the predictors included of multivariate adaptive regression splines, multiple linear regressions, random forests (RF), regression decision tree (RDT), RDT with ensemble and bagger, and gradient boosting machine. BBD, GLM, and Sobol’s total and first-order indices indicated HR as the most important and sensitive predictor in the joint optimizations. GLM pointed to a three-way interaction among HR, BR, and Atm, while BBD, and Sobol’s second-order index showed a two-way interaction between HR and BR as the most important ones. RF outperformed the other full models for all the responses in terms of validation metrics. RF showed the two most important predictors as Temp and BR for RM; HR and Temp for DSC; and Temp and HR for DTG, respectively, which also constituted the most important two-way interactions.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:463-474
    DOI: 10.1016/j.renene.2019.11.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119317197
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2019.11.038?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. Intergovernmental Panel on Climate Change IPCC, 2008. "Intergovernmental Panel on Climate Change: Fourth Assessment Report: Climate Change 2007: Synthesis Report," Working Papers id:1325, eSocialSciences.
    2. Li, Y. & Zhou, L.W. & Wang, R.Z., 2017. "Urban biomass and methods of estimating municipal biomass resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1017-1030.
    3. Xie, Candie & Liu, Jingyong & Zhang, Xiaochun & Xie, Wuming & Sun, Jian & Chang, Kenlin & Kuo, Jiahong & Xie, Wenhao & Liu, Chao & Sun, Shuiyu & Buyukada, Musa & Evrendilek, Fatih, 2018. "Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks," Applied Energy, Elsevier, vol. 212(C), pages 786-795.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    5. Roni, Mohammad S. & Chowdhury, Sudipta & Mamun, Saleh & Marufuzzaman, Mohammad & Lein, William & Johnson, Samuel, 2017. "Biomass co-firing technology with policies, challenges, and opportunities: A global review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1089-1101.
    6. Faaij, Andre P.C., 2006. "Bio-energy in Europe: changing technology choices," Energy Policy, Elsevier, vol. 34(3), pages 322-342, February.
    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. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. 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).
    3. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    4. Song, Weiming & Zhou, Jianan & Li, Yujie & Yang, Jian & Cheng, Rijin, 2021. "New technology for producing high-quality combustible gas by high-temperature reaction of dust-removal coke powder in mixed atmosphere," Energy, Elsevier, vol. 233(C).
    5. 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).
    6. Jing, Rui & He, Yang & He, Jijiang & Liu, Yang & Yang, Shoubing, 2022. "Global sensitivity based prioritizing the parametric uncertainties in economic analysis when co-locating photovoltaic with agriculture and aquaculture in China," Renewable Energy, Elsevier, vol. 194(C), pages 1048-1059.
    7. Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
    8. Rahimi, Mohammad & Abbaspour-Fard, Mohammad Hossein & Rohani, Abbas, 2021. "A multi-data-driven procedure towards a comprehensive understanding of the activated carbon electrodes performance (using for supercapacitor) employing ANN technique," Renewable Energy, Elsevier, vol. 180(C), pages 980-992.

    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. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    3. Danilo Arcentales-Bastidas & Carla Silva & Angel D. Ramirez, 2022. "The Environmental Profile of Ethanol Derived from Sugarcane in Ecuador: A Life Cycle Assessment Including the Effect of Cogeneration of Electricity in a Sugar Industrial Complex," Energies, MDPI, vol. 15(15), pages 1-24, July.
    4. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    5. Zarsky, Lyuba, 2010. "Climate-Resilient Industrial Development Paths: Design Principles and Alternative Models," Working Papers 179080, Tufts University, Global Development and Environment Institute.
    6. Gasol, Carles M. & Martínez, Sergio & Rigola, Miquel & Rieradevall, Joan & Anton, Assumpció & Carrasco, Juan & Ciria, Pilar & Gabarrell, Xavier, 2009. "Feasibility assessment of poplar bioenergy systems in the Southern Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 801-812, May.
    7. Vasiliki Tzelepi & Myrto Zeneli & Dimitrios-Sotirios Kourkoumpas & Emmanouil Karampinis & Antonios Gypakis & Nikos Nikolopoulos & Panagiotis Grammelis, 2020. "Biomass Availability in Europe as an Alternative Fuel for Full Conversion of Lignite Power Plants: A Critical Review," Energies, MDPI, vol. 13(13), pages 1-26, July.
    8. Chhabra, Vibhuti & Bambery, Keith & Bhattacharya, Sankar & Shastri, Yogendra, 2020. "Thermal and in situ infrared analysis to characterise the slow pyrolysis of mixed municipal solid waste (MSW) and its components," Renewable Energy, Elsevier, vol. 148(C), pages 388-401.
    9. Benjamin Jones & Michael Keen & Jon Strand, 2013. "Fiscal implications of climate change," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 20(1), pages 29-70, February.
    10. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    11. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    12. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    13. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    14. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    15. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    16. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    17. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    18. Thyrel, M. & Samuelsson, R. & Finell, M. & Lestander, T.A., 2013. "Critical ash elements in biorefinery feedstock determined by X-ray spectroscopy," Applied Energy, Elsevier, vol. 102(C), pages 1288-1294.
    19. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    20. Zhang, Yun-Long & Liu, Lan-Cui & Kang, Jia-Ning & Peng, Song & Mi, Zhifu & Liao, Hua & Wei, Yi-Ming, 2024. "Economic feasibility assessment of coal-biomass co-firing power generation technology," Energy, Elsevier, vol. 296(C).

    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:renene:v:151:y:2020:i:c:p:463-474. 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.journals.elsevier.com/renewable-energy .

    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.