Smelting Analysis Management System Based On Path Optimization

In recent years,the domestic steel market has become increasingly competitive.The problem stems from the overcapacity caused by too many steel mills.On the other hand,the quality of steel still needs to be strengthened,and high-end steel is lacking.Therefore,improving product quality and stability is an important way to solve the problem of overcapacity and improve the competitiveness of enterprises.The Smelting Analysis Center is responsible for analyzing the elemental content of products in the ironmaking and steelmaking processes.The main steps are sampling,sample delivery,sample preparation,analysis,and data transfer.Perfecting the automation of smelting analysis can generate data quickly and steadily,and provide accurate data feedback for the ironmaking steelmaking process.At the same time,the accumulation and processing of analytical data can provide powerful data support for process improvement.The smelting analysis management system designed for this project provides a solution for automation of steel plant smelting analysis.The carrier loading sample is sent to the smelting analysis site through the pneumatic pipe driven by compressed air,the receiving station unloads the sample,and the sample is sent to the automatic milling machine or the slag sample preparation machine for sample preparation through a CT and a belt trolley.Transmit data to analysis instrument through belt or ST for data analysis.The generated data files are transmitted to the server for storage and fed back to the ironmaking steelmaking site.At the same time,intuitive instructional data is generated through machine learning algorithms to provide data support for improving the process.In the entire automation process,the ant colony algorithm was introduced to optimize the path for sample delivery,preparation,and analysis,ensuring that mechanical wear is reduced,process time is reduced,and the accuracy,stability,and safety of the analyzed data are increased.The subject software system adopts a three-tier C/S model structure.The structure is composed of three layers:client,application server,and DBMS server.The client is used to implement presentation logic,and the application server is mainly used to implement path planning calculations.The implementation of machine learning algorithms,DBMS server is responsible for data processing.The system software is developed using open source Qt.The client,application server,and DBMS server are all implemented by Qt.Qt has cross-platform development capabilities,and at the same time it can develop handheld clients in the future,providing good convenience for on-site operations.Use MySQL as a database for data processing.