Research on Processing Quality Control Model Based on Regression Analysis
DOI:
https://doi.org/10.54097/v4ndw893Keywords:
Processing Quality Control, Multiple Regression, Interactive Regression Analysis, Goodness of Fit Test.Abstract
This article mainly studies the mathematical relationship between system temperature and the quality of finished ore processing products, while the variables of raw ore parameters, processing voltage, and processing water pressure remain unchanged. By establishing daily unified production batch processing data and visually analyzing data characteristics through charts, and visualizing the interaction between different temperature systems, a multiple interaction regression analysis model based on system temperature and processing quality indicators is selected. And through goodness of fit p-value testing, interval prediction with confidence, and sensitivity analysis, the model is sensitivity optimized. An interactive effect diagram and a visual and controllable product quality control program are created for the raw ore quality, system temperature, and quality index qualification rate. The theoretical value with the highest ore processing efficiency is deduced, which can effectively improve resource utilization and yield rate in the ore processing process.
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