Author(s)

Sha Yuan ¹

Affiliation(s)

¹ School of Mathematics and Computer Science, Yan'an University, Yan'an, 716099, China

Corresponding Author

Sha Yuan

ABSTRACT

China is a large country of mineral resources, but its resource endowment is poor and its per capita share is not high. In order to increase the output of ores, respond to national policies, directly or indirectly save non renewable mineral resources and energy required for processing, so as to promote energy conservation and emission reduction, and help achieve the goal of "double carbon". This paper studies the quality control of ore processing, builds a model, and optimizes the model, so as to effectively improve the quality of ore processing and improve the utilization rate of ore.This paper studies the production and processing data of the workshop in the past 10 days. Without considering the influence of voltage, water pressure and other conditions, the product quality results are predicted from the known data. Through the correlation between system I and system II temperature, raw ore parameters 1, 2, 3, 4 and product quality, a BP neural network model is established, and the model analysis, inspection and improvement are carried out to obtain the product quality prediction results.Without considering other conditions, predict the temperature of System I and System II according to the known data, observe and analyze the correlation between the product quality and raw ore parameters and the temperature of System I and System II, establish an inverse model, which is also a BP Shenjing network model, and solve the model, analyze and test it to obtain the most possible temperature prediction results of System I and System II.According to the correlation between system I and system II temperature, raw ore parameters, process data 1, 2, 3, 4 and product quality index ABCD, a decision tree model is established, evaluate and optimize, and predict the product quality qualification rate with the greatest possibility.

KEYWORDS

Linear regression, residual analysis, BP neural network model, decision tree model optimization

CITE THIS PAPER

Sha Yuan, Decision Scheme for Predicting the Quality Control of Ore Processing. Industrial Engineering and Innovation Management (2022) Vol. 5: 1-10. DOI: http://dx.doi.org/10.23977/ieim.2022.051201.

REFERENCES

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