Spinning process intelligent optimized design method based on immune neural network

Abstract

The invention relates to a spinning process intelligent optimized design method based on an immune neural network. Production data are processed and analyzed by utilizing an immune optimized neural network establishing model to obtain a reasonable configuration scheme of a spinning production line parameter; then, the model and the scheme are integrated in an expert system and carry out online connection with the production line to synchronously modify the immune neural network model and the expert system according to real-time production data; the uniform configuration is carried out on all parameters on the spinning production line, and the production process is effectively optimized in time according to the running condition of the production line. A result set is analyzed and evaluated by adopting the spinning process expert system and used for guiding production, and a knowledge library and a rule set of the expert system can be continuously added and improved so that the system has a self-learning function.

Description

technical field [0001] The invention belongs to the technical field of fiber spinning production, in particular to an intelligent optimization design method based on an immune neural network expert system for a differential fiber spinning process. Background technique [0002] Fiber production is a highly complex industrial process whose products include various fibers and their products. It requires high-precision, long-term continuous production, so it has high requirements on the environment and process design of the production line and each link of the production line. For ordinary chemical fiber manufacturers, most of the basis for their production line process design comes from experience and field design manuals. For the sake of insurance, the scheme adopted in the process design of the production line often leaves a large amount of redundancy, which generally accounts for 20% to 30% of the total production capacity, resulting in waste of equipment investment and pow...

AI Technical Summary

Problems solved by technology

These methods belong to the optimization of local processes, without a unified and definite guidance system for configuration, so the optimization effect is limited, it is not easy to cooperate with the production line as a whole, and it is impossible to obtain effective information to guide the further optimization of production through the operation of the production line, which limits the Further development of the production line

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