Stochastic optimization models for energy management in carbonization process of carbon fiber production. (15th November 2015)

• Record Type: Journal Article
• Authors: Khayyam, Hamid; Naebe, Minoo; Bab-Hadiashar, Alireza; Jamshidi, Farshid; Li, Quanxiang; Atkiss, Stephen; Buckmaster, Derek; Fox, Bronwyn
• Is Part Of: Applied energy. Volume 158(2015)
• Page Start: 643
• Page End: 655
• Publication Date: 2015-11-15
• Subjects: HT furnace energy management -- Stochastic optimization models -- Genetic algorithm -- Convex Hull -- Mixed integer linear programming -- Carbonization process; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.08.008
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Seven factors need to be considered simultaneously for energy optimization. Stochastic optimization model can be used for process control. Convex Hull method could model and predict the energy consumptions properly. MILP can optimize the energy consumptions in a given range of production quality. 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. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.