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Design of Optimal Control System for the Whole Process of Polyester Fiber

In this paper, an optimal control system of the whole process of polyester fiber is proposed. Based on the hierarchical Bayesian CTAP model, the system divides fiber production planning into on-demand manufacturing and product development. On-demand manufacturing includes three steps. Firstly, the system collects data from raw material database, equipment database and process database in database cluster. Secondly, immune endocrine neural network (IENN) is used to get the system optimal product process parameters. Finally, a real-time database is used to compensate for the forward error of the production process. In the process of product development, the database of raw materials, equipment and process are regenerated based on the parameter sets of polymerization sub-model, melt transport sub-model and spinning sub-model. Then, IENN is used to get the optimal product process parameters, and a real-time database is used to compensate for the reverse error of the production process. This system can quickly respond to fiber manufacturing needs. On the premise of diversification of enterprise demand and differentiation of competition, it can save energy and reduce consumption, ensure production capacity in a short time, and produce different kinds of polyester fibers with lower cost and higher quality.