Concentrate yield online prediction method

Abstract

A concentrate yield online prediction method is characterized by including the steps that firstly, process index data of ore dressing working procedures and corresponding concentrate yield actual values are acquired; secondly, the acquired process index data of the ore dressing working procedures and the acquired corresponding concentrate yield actual values are combined; thirdly, concentrate yield values are online predicted in real time. According to the method, based on parameter self-tuning, the acquired process index data of the working procedures in the ore dressing production process are updated and then added to a training sample set for training participation, and a model can be online updated. According to different attributes of the process index data of the working procedures, parameters of the concentrate yield prediction model are adjusted in a self-adaptive mode, the parameters of the model are online corrected, the prediction model can well adapt to changeful working conditions, prediction accuracy of the concentrate yield is further improved, preparation engineers can make more reasonable decisions, and therefore enterprise revenues can be maximum.

Description

technical field [0001] The invention belongs to the field of predictive control, and in particular relates to an online predictive method for concentrate output. Background technique [0002] The beneficiation process is an extremely complicated industrial process, which involves multiple beneficiation process processes such as shaft furnace, ore grinding and magnetic separation. At the same time, each process is interactively coupled, involving multiple fields such as physics and chemistry. The prediction result is an important decision-making basis for the production operation of workers in the beneficiation process and the adjustment of production plan indicators by engineers. If the concentrate output cannot be accurately predicted according to the current production conditions, the actual production value of the concentrate output will be seriously affected. It can be said that the forecast results of concentrate output have an important impact on the actual concentrate...

AI Technical Summary

Problems solved by technology

Because the historical production data is offline data, and the problem faced by the model established by offline data is that with the arrival of new working condition sample data, the forecast model has not been updated in real time, but the historical data is still used to predict new samples. Therefore, as The prediction accuracy will also change with the change of working conditions, or even become worse

In addition, even if some prediction models adopt online training and online prediction methods, such as online support vector machine prediction methods, the penalty parameter C in the traditional online support vector machine-based ore dressing process concentrate output prediction model is fixed. Change, that is to say, when the training error exceeds the soft interval ε, the model will give the same punishment to the sample, that is, the importance of the sample is regarded as the same, that is, the default working condition is the same

However, for complex industrial processes, the operating conditions cannot be constant, so this approach is actually unreasonable

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