An Online Prediction Method of Concentrate Production

Abstract

An online prediction method for concentrate output, which method includes: Step 1: Collect the process index data of each process of mineral processing and the corresponding actual value of concentrate output; Step 2: Collect the process index data of each process of mineral processing and the corresponding actual value The actual value of the concentrate output is combined and processed; Step 3: Predict the concentrate output value online in real time; the method of the present invention is based on parameter self-tuning, and the process index data of each process collected during the mineral processing production process is updated and the training samples are added in a timely manner to participate in the training , realize online update of the model; according to the different attributes of the process index data of each process, adaptively adjust the parameters of the concentrate production prediction model and realize online modification of the model parameters, which can make the prediction model better adapt to changing working conditions. Conditions further improve the prediction accuracy of concentrate production, and mineral processing engineers can make more reasonable decisions, thereby maximizing corporate benefits.

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

Images