• Energy Research

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.

Carbon fiber is an advanced material with high tensile strength and modulus, ideally suited for light weight applications. Carbon fiber properties are directly dependent on all aspects of production, especially the process step of thermal stabilization. Stabilization is considered to be one of the most critical process steps. Moreover, the stabilization process is the most energy consuming, time consuming and costly step. As oxidation is an exothermic process, constant airflow to uniformly remove heat from all tows across the towband is indispensable. Our approach is to develop an intelligent computational system that can construct an optimal Computational Fluid Dynamics (CFD) solution. In this study, an electrical heater has been designed by CFD modeling and intelligently controlled. The model results show that the uniform airflow and minimum turbulence kinetic energy can be achieved by combining intelligent system technology with CFD analysis strategy.

Carbon fibre composites offer considerable potential for mass reduction in automotive applications. However, raw material cost is one of the major factors that constraints its extensive use in this mass market. Here we report a systematic study that presents the cost contributors by considering the entire process chain of the carbon fibre manufacturing. The sensitivity analysis revealed that the final cost of Polyacrylonitrile (PAN) precursor and carbon fibres were strongly influenced by tow size. It was observed that a prompt decrease in the precursor and carbon fibre cost per kg for tow sizes from 3k to 12k, later this decrement was gradual and almost became stable above 50k. Moreover, with an increase in tow size from 3k to 50k, the contribution of the precursor on the final carbon fibre cost decreased from 76.6% to 49.6%. On the other hand, the contribution of the other factors increased with increase in the tow size, for instance, labour (9.86%-17.78%), Energy (2.49%-6.48%) and Depreciation (6.11%-11.01%). Nevertheless, precursor holds the major share in determining the final price of the carbon fibres.