Method for predicting performance indexes in polyester fiber spinning process

Abstract

The invention relates to a method for predicting performance indexes in a polyester fiber spinning process, in particular to a method for predicting performance indexes in a polyester fiber spinning process based on a least square support vector machine (LS-SVM) optimized by a particle swarm optimization (PSO) algorithm improved by a power law. The method comprises the following steps of selecting production parameters, including a spinning speed, a spinning temperature, an air blowing temperature and an air blowing speed, in the polyester fiber spinning process as feature information, performing linear function normalization, and establishing an input sample dataset; determining main performance indexes, including half-fold elongation rate, irregularity of the half-fold elongation rate, breaking strength and elongation capability, influencing polyester fiber quality, performing logarithmic function normalization, and establishing an output sample dataset; and building an LS-SVM model according to the input and output sample datasets, adopting a Gauss radial basis function (RBF) as a kernel function of the LS-SVM, and selecting an optimal penalty factor C and a kernel function parameter sigma by using PSO. The PSO process is improved according to the power law, so that the optimization speed can be greatly increased and accurate prediction is realized.

Description

technical field [0001] The invention belongs to the technical field of intelligent production prediction of polyester fibers, and relates to a method for predicting performance indexes in the spinning process of polyester fibers, in particular to a prediction method in the spinning process of polyester fibers based on the improved PSO optimized LS-SVM based on the power law law . Background technique [0002] Polyester, as the most common fiber in life, has a wide range of applications. The spinning process of polyester fiber is a complex process with multiple parameters, multiple environments, and multiple processes. Different materials in different shapes are spun into polyester filaments in proportion through processes such as fusion, blowing, drafting, and collection. The spinning process, also known as the coagulation forming process, is the most critical link in the entire production process. The spinning process is a process in which the polymer stock solution is ex...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is that in the current polyester fiber production process, the corresponding relationship between the main process parameters of the spinning process and product performance indicators often relies on production experience, lacks relevant theoretical guidance, and online debugging requires the production line to be shut down and carried out. Multiple production simulations will bring large economic losses, etc., and a method for predicting performance indicators in the polyester fiber spinning process is proposed. In-process prediction methods

Images