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

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

Applied Energy, 2015, vol. 158, issue C, 643-655

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.

Keywords: HT furnace energy management; Stochastic optimization models; Genetic algorithm; Convex Hull; Mixed integer linear programming; Carbonization process (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2015.08.008

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