Online prediction method for RSA (Ratio of Soda to Aluminate) of outlet solution of two-stream-process alumina dissolution process
A technology of causticity ratio and process export, which is applied in special data processing applications, instruments, electrical digital data processing, etc., and can solve problems such as long training time, difficult, and long-term effective application
Active Publication Date: 2015-02-04
CENT SOUTH UNIV
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[0003] At present, there is already a method for predicting the causticity ratio of the dissolution solution that combines the mechanism model with the neural network model. Both generalization ability and prediction accuracy need to be improved, making it difficult to apply this method effectively in the actual production process for a long time
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[0041] like figure 1 As shown, the present invention realizes the prediction of the causticity ratio of the eluate at the exit of six flashes by using the mechanism model, the method of correcting the prediction value of the mechanism model based on fuzzy expert rules, and the error time series prediction model based on the least squares support vector machine. Here, 422 data from a factory’s continuous production are selected for case analysis, in which the first 300 groups are used for training, and the last 122 groups are used for prediction.
[0042] 1. Predicted value of caustic ratio mechanism model
[0043] In this embodiment, the main chemical reactions in the stripping process are the reaction of lye and alumina to generate sodium aluminate; the reaction of alumina and silicon oxide and lye to generate sodium aluminosilicate; the reaction of titanium oxide and calcium oxide to generate insoluble titanium calcium oxide; a part of calcium oxide reacts with alumina and ...
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The invention discloses an online prediction method for RSA (Ratio of Soda to Aluminate) of an outlet solution of a two-stream-process alumina dissolution process. The method comprises the following steps: (1) obtaining a mechanism model predicted value based on material balance; (2) amending the mechanism model predicted value based on fuzzy expert rules according to the amounts of change of temperature and pressure difference under current work conditions, so as to obtain an amended mechanism model predicted value; (3) establishing an error time series prediction model based on a least squares support vector machine according to a historical error time series formed by an RSA measured value and the amended mechanism model predicted value, obtaining an error predicted value of next moment by using the prediction model, and compensating the amended mechanism model predicted value by using error predicted values, so as to obtain a predicted value for the RSA of the solution. The method has the advantages that the relative error of predicted outlet solution RSA is smaller than + / -2%, and the maximum absolute error is smaller than 0.025.
Description
technical field [0001] The invention relates to an online prediction method for the caustic ratio in the dissolution process of alumina double-flow method. Background technique [0002] In the production of Bayer process alumina, high pressure dissolution is an extremely critical process. The causticity ratio of the eluate in the high-pressure dissolution process is a very important economic and technical index. It not only reflects the alkali consumption of alumina, but also has a great impact on the subsequent production of alumina. In order to improve the subsequent production conditions of alumina and increase the output and quality of products, the key is to be able to detect the caustic ratio on-line and optimize the ratio on this basis. However, at present, there is no instrument that can directly detect the caustic ratio, but it is calculated on the basis of chemical analysis of the raw ore pulp and the dissolved ore pulp. Since the chemical analysis takes a long ti...
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Inventor 谢永芳王晓丽尉思谜阳春华桂卫华谢森徐德刚王峰
Owner CENT SOUTH UNIV