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Stochastic optimization models for energy management in carbonization process of carbon fiber production

Author

Listed:
  • Khayyam, Hamid
  • Naebe, Minoo
  • Bab-Hadiashar, Alireza
  • Jamshidi, Farshid
  • Li, Quanxiang
  • Atkiss, Stephen
  • Buckmaster, Derek
  • Fox, Bronwyn

Abstract

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov–Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:158:y:2015:i:c:p:643-655
    DOI: 10.1016/j.apenergy.2015.08.008
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    Cited by:

    1. Guangxiao Hu & Xiaoming Ma & Junping Ji, 2017. "A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China," Sustainability, MDPI, vol. 9(11), pages 1-12, October.
    2. 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).
    3. Srinivas Nunna & Maxime Maghe & Seyed Mousa Fakhrhoseini & Bhargav Polisetti & Minoo Naebe, 2018. "A Pathway to Reduce Energy Consumption in the Thermal Stabilization Process of Carbon Fiber Production," Energies, MDPI, vol. 11(5), pages 1-10, May.
    4. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    5. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.

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