Khashayar Badii

Energy Saving in Electric Heater of Carbon Fiber Stabilization Oven

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.

A Computational Fluid Dynamic Model for Prediction of Organic Dyes Adsorption from Aqueous Solutions

Modelling of the removal of synthetic dyes from aqueous solutions by adsorbents is important to develop an appropriate treatment plan using adsorption process. This paper presents a computational fluid dynamic model incorporating the Langmuir isotherm scheme and second-order kinetic expression to describe the adsorption process. The governing equation of the model was numerically solved using PHOENICS software to simulate synthetic dyes adsorption from the aqueous system. The experimental results presented in this study and taken from the literature for the removal of synthetic dyes were compared with those results predicted by the numerical model. The predicted outputs of the model match the experimental measurements satisfactory. A sensitivity analysis of the major parameters that influence the percent of dye removal from solution phase has been carried out. Three of the main parameters taken into account were the kinetic rate constant, amount of dye adsorbed at equilibrium and the Langmuir isotherm constant. It was found that the model is most sensitive to the amount of dye adsorbed at equilibrium. This effect is most obvious at the early stages of the adsorption process when the rate of dye removal is very fast. Quantification of the reaction mechanism allows developing an appropriate remediation strategy based on the adsorption process.

Support vector regression modelling and optimization of energy consumption in carbon fiber production line

Abstract The main chemical industrial efforts are to systematically and continuously explore innovative computing methods of optimizing manufacturing processes to provide better production quality with lowest cost. Carbon fiber industry is one of the industries seeks these methods as it provides high production quality while consuming a lot of energy and being costly. This is due to the fact that the thermal stabilization process consumes a considerable amount of energy. Hence, the aim of this study is to develop an intelligent predictive model for energy consumption in thermal stabilization process, considering production quality and controlling stochastic defects. The developed and optimized support vector regression (SVR) prediction model combined with genetic algorithm (GA) optimizer yielded a very satisfactory set-up, reducing the energy consumption by up to 43%, under both physical property and skin-core defect constraints. The developed stochastic-SVR-GA approach with limited training data-set offers reduction of energy consumption for similar chemical industries, including carbon fiber manufacturing.

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.