Rotary kiln sintering state recognition method with artificial feedback regulation mechanism

Abstract

The invention discloses a rotary kiln sintering state recognition method with an artificial feedback regulation mechanism. The method is characterized by comprising the following steps: the first stage of extracting a cognition feature of a flame image to establish a complete recognition feature space of a sample; the second stage of establishing a reduction cognition feature space based on information granularity; the third stage of designing a cognition system through the adoption of an integrated RVFL classifier; the fourth stage of reliably evaluating a cognition result and freely regulating the information granularity according to an evaluation result, re-activating the second to the fourth stage, updating the reduction cognition feature space and feeding back the cognition repeatedly. Through the adoption of the method disclosed by the invention, the multi-level feedback cognition can be performed through the artificial free regulation of the complete cognition feature level, the cognition precision and stability are obviously improved, thereby really realizing the operation of replacing the manual fire watching by machine fire watching; the method lays the foundation for realizing the closed ring control of a rotary kiln clinker quality index.

Description

technical field [0001] The invention belongs to the technical field of flame image recognition of a rotary kiln, and in particular relates to a method for identifying a firing state of a rotary kiln with an imitation feedback adjustment mechanism. Background technique [0002] Rotary kiln is a large-scale sintering equipment widely used in metallurgy, environmental protection, chemical industry and cement. Due to the difficulties in the sintering process of the rotary kiln, such as the difficulty of online measurement of the clinker quality index and the difficulty of accurately identifying the firing state of key process parameters closely related to the clinker quality index, the existing sintering process of the rotary kiln is still in the "manually watching the fire" Open-loop operation phase. However, the results of manual identification of the firing state are restricted by subjective factors such as operator experience, responsibility, and attention, which can easily...

AI Technical Summary

Problems solved by technology

[0007] The open-loop cognitive system extracts simple features for different flame images at a specific granularity level. Once the feature space is established, it will not be updated, which will easily lead to the fact that the simple feature space is not enough to fully represent the flame image.

In addition, the classifier is not robust enough to establish classification cognition criteria only by comparing peak signal-to-noise ratio values.

The method used does not conform to the characteristics of using multi-level features to repeatedly deliberate and compare when human beings judge the firing state, and the recognition accuracy is not satisfactory

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