Content area
As a key parameter in the sintering process, the ferrous oxide content of sinter can reflect the working condition, energy consumption level, and quality level of the final sintered products in the sintering process. It has become a key problem to realize the prediction of ferrous oxide content in sinter and feedback control of sinter quality accordingly. The two commonly used methods for detecting ferrous oxide content in industrial production currently do not meet real-time requirements and cannot provide timely feedback for production regulation. Therefore, research on real-time prediction technology of ferrous oxide content in sinter was carried out, and an optimized back propagation neural network model was established to realize the mapping between characteristic parameters and the FeO content in sinter. The characteristic parameters include image parameters and process parameters. Through the research on the brightness change trend of the machine tail cross-section image, the best cross-section image acquisition method based on brightness difference is realized, and image parameters are obtained by image processing technology. The process parameters were selected using correlation analysis. Through data processing techniques such as data cleaning, normalization, and feature fusion, feature parameters were obtained as input vectors for the neural network. To improve prediction accuracy and system stability, an adaptive learning rate and genetic algorithm were used to optimize the traditional BP neural network. The average test error of the optimized prediction model was 0.32%. Taking actual data production as an example, test data on the FeO content of sinter were extracted from the laboratory. Compared with the FeO content predicted by the system, the prediction time of the system was about 2 h earlier than the test time. In terms of prediction accuracy, the average absolute error was 0.25%, and the absolute prediction error was not more than ±1%.
Details
Working conditions;
Iron compounds;
Correlation analysis;
Brightness;
Back propagation networks;
Image processing;
Steel industry;
Systems stability;
Machine learning;
Energy consumption;
Prediction models;
Industrial production;
Cameras;
Cross-sections;
Accuracy;
Image acquisition;
Errors;
Vectors;
Algorithms;
Real time;
Feedback control;
Process parameters;
Testing time;
Data processing;
Data analysis;
Feedback;
Sintering;
Raw materials;
Genetic algorithms;
Oxidation;
Neural networks;
Process controls;
Adaptive learning
; Zhu Yanlong 1 1 Tianjin Research Institute for Water Transport Engineering, M.O.T, Tianjin 300456, China; [email protected] (S.L.); [email protected] (Y.C.); [email protected] (Z.Z.)
2 School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China; [email protected]