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.