Process-batch-model-based prediction method of oxygen flow rate for converter

Abstract

The invention discloses a process-batch-model-based prediction method of an oxygen flow rate for a converter. A process state of a converter is calculated according to a production process and an operation system of the converter; on the basis of a standard batch curve of the converter, the process state of the converter is converted into a starting point of the converter at a current standard batch curve time point; and with a counter, an actual time point is calculated, thereby predicting an oxygen flow rate of the converter. Moreover, a corresponding standard curve can be switched automatically according to the number of times of reblowing of the converter, thereby improving prediction accuracy. Compared with the universal model, the response speed is fast and the prediction precision is high; and construction implementation can be carried out conveniently.

Description

technical field [0001] The present invention relates to the field of information technology, and relates to the production process, operation system and batch model-based prediction technology of the converter, which is applied to the oxygen optimization scheduling of the iron and steel energy management and control system, and is used to alleviate the shortage of oxygen and reduce the release of oxygen, and specifically relates to a A Prediction Method of Oxygen Flow Rate for Converter Based on Process Batch Model. Background technique [0002] Iron and steel enterprises are large energy consumers. Reasonable use of energy and reduction of energy emissions are the goals of the iron and steel industry. Corresponding to the oxygen dispatching system in the iron and steel industry, in order to achieve a balance between supply and use, at present it mainly relies on on-site dispatchers for deployment. Oxygen generating units are high energy consumption systems. In the metallur...

AI Technical Summary

Problems solved by technology

At present, there are several difficulties in the calculation of the converter state: 1) The signal of the converter control system is not complete. For example, during the loading process, the crane will move to the accessories of the converter, but the position signal of the drive is generally not in the control system; There is no weight signal control system, and it is relatively difficult to judge the current state of the converter if these signals cannot be obtained by the energy management and control system.

2) The amount of data connected to the energy management and control system is not too much, which makes it difficult to calculate the state

3) The signals of different converters are not exactly the same, such as the blowing point of the oxygen lance, some control systems have this measuring point, but some converter control systems do not have this measuring point; 4) The general converter state calculation does not consider each step It is also difficult to distinguish the steps of blowing and supplementary blowing; 5) In terms of timing logic and key signal inclination angle processing, if the logic is not strict, it is easy to misjudge the state, such as the general logic that the converter Loading is within the range of the loading angle, but the converter may also be in this angle range when it is under maintenance

[0008] In the prior art, a general prediction method is used to predict the oxygen flow rate of the converter. This scheme has the following problems: the oxygen used by the converter itself has batch rules, and the oxygen flow rate used in a smelting cycle varies greatly, and is affected by the production process. Affected by various factors in the process, the general modeling method is not very accurate in predicting the oxygen flow rate for top blowing of the converter, and the response to the state change of the converter is not very timely.

[0009] The invention patent "An online energy forecasting system and method based on the product ARIMA model" proposes an online energy forecasting technology based on the ARIMA method, which is suitable for stationary, non-stationary, seasonal fluctuations, etc., but still cannot adapt to the oxygen consumption of the converter. A personalized batch scene

Although the neural network model has a high nonlinear mapping ability and can approximate nonlinear functions with arbitrary precision, there are some problems in the actual calculation: (1) The convergence speed of the calculation process of backpropagation is slow; (2) ) There is a minimum value of the energy function; (3) The selection of the number of hidden neurons and the connection weight often depends on experience; (4) The convergence of the network is related to the structure of the network; (5) The engineering application is relatively complicated, Difficult to be mastered by ordinary engineers and technicians

The application number is 200610113685.6. This patent provides an integrated online energy forecasting system and method for iron and steel enterprises, using a variety of energy forecasting algorithms, such as linear regression model, nonlinear regression model, principal component regression model, PLS modeling, support Vector machine modeling, expert knowledge modeling, neural network modeling, time series modeling, wavelet transform modeling, gray system GM modeling and custom combination modeling, etc., using combination models for energy forecasting, but without batch Sub-models, limited prediction accuracy for personalized batch scenarios

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