Development of a predictive model for study of skin-core phenomenon in stabilization process of PAN precursor

Studying the presence and progress of fiber defects, such as skin-core structure, is an important tool for analysis of a chemical process. In this article, the skin core morphology has been analyzed by optical microscopic (OM) images and Fourier transform infrared attenuated total reflectance mapping (FTIR-ATR mapping). The results of FTIR-ATR mapping showed that the fiber is almost uniform in the core area while OM images are accurate enough to be used for skin-core analysis. Using OM images, the core ratio of samples were measured to quantify the skin-core structure. Non-parametric kernel density estimation methods have then been compared with conventional parametric distribution models using these data. The results reveal that the parametric methods cannot adequately describe the skin-core phenomenon and that the non-parametric distributions are more appropriate for the quantification of skin-core morphology. By applying the non-parametric distributions, a model has been developed, which describes the relationship between the skin-core defect and the operation parameters of the fiber production. This approach can be used to predict the probability of skin-core occurrence and can be used to decrease the presence of this phenomenon in the carbon fibers production industry. Our results show that temperature is one of the most significant operational parameter at a typical oxygen concentration (in air at atmospheric pressure) governing the skin-core formation

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

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