IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v225y2021ics0360544221003625.html
   My bibliography  Save this article

Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning

Author

Listed:
  • Khayyam, Hamid
  • Naebe, Minoo
  • Milani, Abbas S.
  • Fakhrhoseini, Seyed Mousa
  • Date, Abhijit
  • Shabani, Bahman
  • Atkiss, Steve
  • Ramakrishna, Seeram
  • Fox, Bronwyn
  • Jazar, Reza N.

Abstract

There remain major concerns over the increasing use and waste of materials and energy resources in multiple manufacturing sectors. To address these concerns, some manufacturers have begun to align their R&D efforts with the circular economy principles: Reduce, Reuse, Recycle and Replace (RRRR). Focusing on advanced composites manufacturing sector, this paper presents an innovative approach for process design and analysis of a new waste heat recovery system for carbon fiber manufacturing. Namely, the stabilization process is known to be one of the most critical steps in the production of carbon fibers, as it consumes the most energy, has the largest factory footprint, is a complex system composed of many components, and is the largest capital investment within the factory line. The heat recovery system in this step of the manufacturing can notably reduce energy consumption, emission, cost, and conversion time, while aiming to maintain the mechanical properties of the final product. Here, via an actual industry-scale fibre production setting, the energy consumption factors were obtained and used to model the total energy and its balance in the thermal stabilization step. Two machine learning approaches with limited data, Artificial Neural Network and Non-Linear Regression were then constructed to predict the energy consumption. Results suggested that using the recovery system by means of a heat exchanger, can yield over 62.7 kW recovery, corresponding to 64% of total exhausted energy from the entire process. The electric energy consumption was reduced from 73.3 kW to 10.2 kW, which corresponded to an 86% improvement in the total energy efficiency. The model also confirmed that, by preheating the make-up air with the recovered energy, the energy performance index of the thermal stabilization can be increased from 0.08 to 0.44, along with a reduction in the process carbon footprint by 28.5 t/y. This is especially crucial as we are turning on smart digitalisation in manufacturing inspired by industry 4.0 concept with limited data.

Suggested Citation

  • Khayyam, Hamid & Naebe, Minoo & Milani, Abbas S. & Fakhrhoseini, Seyed Mousa & Date, Abhijit & Shabani, Bahman & Atkiss, Steve & Ramakrishna, Seeram & Fox, Bronwyn & Jazar, Reza N., 2021. "Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221003625
    DOI: 10.1016/j.energy.2021.120113
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221003625
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120113?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. Khayyam, Hamid & Naebe, Minoo & Bab-Hadiashar, Alireza & Jamshidi, Farshid & Li, Quanxiang & Atkiss, Stephen & Buckmaster, Derek & Fox, Bronwyn, 2015. "Stochastic optimization models for energy management in carbonization process of carbon fiber production," Applied Energy, Elsevier, vol. 158(C), pages 643-655.
    2. Aranguren, P. & Astrain, D. & Rodríguez, A. & Martínez, A., 2015. "Experimental investigation of the applicability of a thermoelectric generator to recover waste heat from a combustion chamber," Applied Energy, Elsevier, vol. 152(C), pages 121-130.
    3. Chowdhury, Jahedul Islam & Hu, Yukun & Haltas, Ismail & Balta-Ozkan, Nazmiye & Matthew, George Jr. & Varga, Liz, 2018. "Reducing industrial energy demand in the UK: A review of energy efficiency technologies and energy saving potential in selected sectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1153-1178.
    4. Macián, V. & Serrano, J.R. & Dolz, V. & Sánchez, J., 2013. "Methodology to design a bottoming Rankine cycle, as a waste energy recovering system in vehicles. Study in a HDD engine," Applied Energy, Elsevier, vol. 104(C), pages 758-771.
    5. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    6. Xu, Z.Y. & Wang, R.Z. & Yang, Chun, 2019. "Perspectives for low-temperature waste heat recovery," Energy, Elsevier, vol. 176(C), pages 1037-1043.
    7. Tian, En & He, Ya-Ling & Tao, Wen-Quan, 2017. "Research on a new type waste heat recovery gravity heat pipe exchanger," Applied Energy, Elsevier, vol. 188(C), pages 586-594.
    8. Chen, Xi & Chen, Qun & Chen, Hong & Xu, Ying-Gen & Zhao, Tian & Hu, Kang & He, Ke-Lun, 2019. "Heat current method for analysis and optimization of heat recovery-based power generation systems," Energy, Elsevier, vol. 189(C).
    9. Moeini Sedeh, Mahmoud & Khodadadi, J.M., 2013. "Energy efficiency improvement and fuel savings in water heaters using baffles," Applied Energy, Elsevier, vol. 102(C), pages 520-533.
    10. Wang, Xuan & Jin, Ming & Feng, Wei & Shu, Gequn & Tian, Hua & Liang, Youcai, 2018. "Cascade energy optimization for waste heat recovery in distributed energy systems," Applied Energy, Elsevier, vol. 230(C), pages 679-695.
    11. Delpech, Bertrand & Milani, Massimo & Montorsi, Luca & Boscardin, Davide & Chauhan, Amisha & Almahmoud, Sulaiman & Axcell, Brian & Jouhara, Hussam, 2018. "Energy efficiency enhancement and waste heat recovery in industrial processes by means of the heat pipe technology: Case of the ceramic industry," Energy, Elsevier, vol. 158(C), pages 656-665.
    12. Wang, Yang & Kuckelkorn, Jens & Li, Daoliang & Du, Jiangtao, 2018. "Evaluation on distributed renewable energy system integrated with a Passive House building using a new energy performance index," Energy, Elsevier, vol. 161(C), pages 81-89.
    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. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.
    2. Shi, Yao & Lin, Runze & Wu, Xialai & Zhang, Zhiming & Sun, Pei & Xie, Lei & Su, Hongye, 2022. "Dual-mode fast DMC algorithm for the control of ORC based waste heat recovery system," Energy, Elsevier, vol. 244(PA).
    3. Kristia Kristia & Mohammad Fazle Rabbi, 2023. "Exploring the Synergy of Renewable Energy in the Circular Economy Framework: A Bibliometric Study," Sustainability, MDPI, vol. 15(17), pages 1-27, September.
    4. Kawtar Rahaoui & Hamid Khayyam & Quoc Linh Ve & Aliakbar Akbarzadeh & Abhijit Date, 2021. "Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
    5. Juraj Šebo & Miriam Šebová & Iztok Palčič, 2021. "Implementation of Circular Economy Technologies: An Empirical Study of Slovak and Slovenian Manufacturing Companies," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    6. Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    7. Kyle Pender & Konstantinos Bacharoudis & Filippo Romoli & Peter Greaves & Jonathan Fuller, 2024. "Feasibility of Natural Fibre Usage for Wind Turbine Blade Components: A Structural and Environmental Assessment," Sustainability, MDPI, vol. 16(13), pages 1-31, June.
    8. Daniel Silva & Ricardo Rocha & Filipe Ribeiro & Helena Monteiro, 2024. "Environmental Impact of an Innovative Aeronautic Carbon Composite Manufactured via Heated Vacuum-Assisted Resin Transfer Molding," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    9. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    10. Elahi, Ehsan & Khalid, Zainab, 2022. "Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms," Applied Energy, Elsevier, vol. 326(C).
    11. Zeinab Farshadfar & Tomasz Mucha & Kari Tanskanen, 2024. "Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    12. Zain Anwar Ali & Mahreen Zain & M. Salman Pathan & Peter Mooney, 2024. "Contributions of artificial intelligence for circular economy transition leading toward sustainability: an explorative study in agriculture and food industries of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19131-19175, August.
    13. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    14. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    15. Rohit Agrawal & Vishal A. Wankhede & Anil Kumar & Sunil Luthra & Abhijit Majumdar & Yigit Kazancoglu, 2022. "An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling," Operations Management Research, Springer, vol. 15(3), pages 609-626, December.

    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. Zauner, Christoph & Windholz, Bernd & Lauermann, Michael & Drexler-Schmid, Gerwin & Leitgeb, Thomas, 2020. "Development of an Energy Efficient Extrusion Factory employing a latent heat storage and a high temperature heat pump," Applied Energy, Elsevier, vol. 259(C).
    2. Yi Ding & Qiang Guo & Wenyuan Guo & Wenxiao Chu & Qiuwang Wang, 2024. "Review of Recent Applications of Heat Pipe Heat Exchanger Use for Waste Heat Recovery," Energies, MDPI, vol. 17(11), pages 1-28, May.
    3. Llera, Rocio & Vigil, Miguel & Díaz-Díaz, Sara & Martínez Huerta, Gemma Marta, 2022. "Prospective environmental and techno-economic assessment of steam production by means of heat pipes in the steel industry," Energy, Elsevier, vol. 239(PD).
    4. Delpech, Bertrand & Axcell, Brian & Jouhara, Hussam, 2019. "Experimental investigation of a radiative heat pipe for waste heat recovery in a ceramics kiln," Energy, Elsevier, vol. 170(C), pages 636-651.
    5. Li, Bo & Huang, Kuo & Yan, Yuying & Li, Yong & Twaha, Ssennoga & Zhu, Jie, 2017. "Heat transfer enhancement of a modularised thermoelectric power generator for passenger vehicles," Applied Energy, Elsevier, vol. 205(C), pages 868-879.
    6. Zare Banadkouki, Mohammad Reza, 2023. "Selection of strategies to improve energy efficiency in industry: A hybrid approach using entropy weight method and fuzzy TOPSIS," Energy, Elsevier, vol. 279(C).
    7. Miguel Castro Oliveira & Muriel Iten & Henrique A. Matos, 2022. "Review on Water and Energy Integration in Process Industry: Water-Heat Nexus," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    8. Ünal, Berat Berkan & Onaygil, Sermin & Acuner, Ebru & Cin, Rabia, 2022. "Application of energy efficiency obligation scheme for electricity distribution companies in Turkey," Energy Policy, Elsevier, vol. 163(C).
    9. He, Chao & Liu, Chao & Zhou, Mengtong & Xie, Hui & Xu, Xiaoxiao & Wu, Shuangying & Li, Yourong, 2014. "A new selection principle of working fluids for subcritical organic Rankine cycle coupling with different heat sources," Energy, Elsevier, vol. 68(C), pages 283-291.
    10. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Polygon generation and video-to-video translation for time-series prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 261-279, January.
    11. Guillaume, Ludovic & Legros, Arnaud & Desideri, Adriano & Lemort, Vincent, 2017. "Performance of a radial-inflow turbine integrated in an ORC system and designed for a WHR on truck application: An experimental comparison between R245fa and R1233zd," Applied Energy, Elsevier, vol. 186(P3), pages 408-422.
    12. Xu, Yang & Li, Ming-Jia & Zheng, Zhang-Jing & Xue, Xiao-Dai, 2018. "Melting performance enhancement of phase change material by a limited amount of metal foam: Configurational optimization and economic assessment," Applied Energy, Elsevier, vol. 212(C), pages 868-880.
    13. Jiang, Jiatong & Hu, Bin & Wang, R.Z. & Liu, Hua & Zhang, Zhiping & Wu, Yongqiang & Yue, Qingxue & Zhang, Ying, 2024. "Film condensation experiments of R1233zd(E) over horizontal tubes and high-temperature condensation predictions for high-temperature heat pump," Energy, Elsevier, vol. 300(C).
    14. Jin Zhu & Dequn Zhou & Zhengning Pu & Huaping Sun, 2019. "A Study of Regional Power Generation Efficiency in China: Based on a Non-Radial Directional Distance Function Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    15. Roberto Pili & Hartmut Spliethoff & Christoph Wieland, 2017. "Dynamic Simulation of an Organic Rankine Cycle—Detailed Model of a Kettle Boiler," Energies, MDPI, vol. 10(4), pages 1-28, April.
    16. Yu Zhai & Xu Zhao & Guanghui Xue & Zhifeng Dong, 2023. "Study on Heat Transfer Performance and Parameter Improvement of Gravity-Assisted Heat Pipe Heat Transfer Unit for Waste Heat Recovery from Mine Return Air," Energies, MDPI, vol. 16(17), pages 1-17, August.
    17. Yan Bai & Xingyi Ma & Jing Zhang & Lei Zhang & Jing Bai, 2024. "Energy Efficiency Assessment and Prediction Based on Indicator System, PSO + AHP − FCE Model and Regression Algorithm," Energies, MDPI, vol. 17(8), pages 1-23, April.
    18. Sovacool, Benjamin K. & Bazilian, Morgan & Griffiths, Steve & Kim, Jinsoo & Foley, Aoife & Rooney, David, 2021. "Decarbonizing the food and beverages industry: A critical and systematic review of developments, sociotechnical systems and policy options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    19. Muhammad Suleman Malik & Naveed Iftikhar & Abdul Wadood & Muhammad Omer Khan & Muhammad Usman Asghar & Shahbaz Khan & Tahir Khurshaid & Ki-Chai Kim & Zabdur Rehman & S. Tauqeer ul Islam Rizvi, 2020. "Design and Fabrication of Solar Thermal Energy Storage System Using Potash Alum as a PCM," Energies, MDPI, vol. 13(23), pages 1-16, November.
    20. Xia, Guanghui & Zhuang, Dawei & Ding, Guoliang & Lu, Jingchao, 2020. "A quasi-three-dimensional distributed parameter model of micro-channel separated heat pipe applied for cooling telecommunication cabinets," Applied Energy, Elsevier, vol. 276(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:energy:v:225:y:2021:i:c:s0360544221003625. 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/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.