Robust random-weight neural network-based molten-iron quality multi-dimensional soft measurement method

A neural network and soft sensor technology, applied in biological neural network models, iron and steel manufacturing processes, computer simulation, etc., can solve the problem of excessive consumables, outliers in measurement data, and inability to reflect the inherent dynamic characteristics of the blast furnace smelting process And other issues

CN105608492AActive Publication Date: 2016-05-25NORTHEASTERN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2016-05-25

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Abstract

The invention relates to a robust random-weight neural network-based molten-iron quality multi-dimensional soft measurement method which belongs to the blast-furnace smelting automatic control field, in particular to a Cauchy distribution weighted M-estimation random-weight neural network (M-RVFLNs) based method for multi-dimensional parameter-dynamic soft measurement of the molten-iron quality in the blast-furnace smelting process. According to the method of the invention, the principal component analysis (PCA) method is adopted to chose main parameters which affect the blast-furnace molten iron quality as model input variables, a molten-iron quality multi-dimensional dynamic prediction model which has an output self-feedback structure and takes into account input-output data at different moments is constructed, and it is possible to carry out multi-dimensional dynamic soft measurement of the main parameters Si content, P content, S content and molten iron temperature which represent the blast-furnace molten iron quality. The method of the invention comprises the following steps of (1) choosing auxiliary variables and determining model input variables and (2) training and using the M-RVFLNs soft measurement model.
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Description

technical field

[0001] The invention belongs to the field of automatic control of blast furnace smelting, and in particular relates to a dynamic soft sensing method for multivariate molten iron quality parameters in the blast furnace ironmaking process based on Cauchy distribution weighted M-estimated random weight neural networks (M-RVFLNs). Background technique

[0002] Blast furnace ironmaking is used to reduce iron from iron ore and other iron-containing compounds to smelt qualified molten iron. The ironmaking process is an extremely complex nonlinear dynamic process. Blast furnace ironmaking reduces iron from iron ore and other iron-containing compounds through complex gas-solid, solid-solid, and solid-liquid reactions in the furnace, and smelts qualified molten iron. At the same time, the quality index of molten iron, as the most important production index in the blast furnace ironmaking process, directly determines the quality of subsequent steel products and the ener...

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Embodiment Construction

[0087] As shown in the figure, the present invention is based on the computer system composition of conventional measurement system, data collector, M-RVFLNs soft sensor software and operating software, and the detailed structure is as follows figure 1 shown. Conventional measuring instruments such as flowmeters, pressure gauges and thermometers are installed in various corresponding positions of the blast furnace smelting system. The data collector is connected to the conventional measurement system, and connected to the computer system running the online forecast software through the communication bus. The conventional measuring system mainly includes the following conventional measuring instruments including:

[0088] Three flowmeters are used to measure the pulverized coal injection volume, oxygen-enriched flow, and cold air flow of the blast furnace pulverized coal injection system on-line;

[0089] A thermometer for online measurement of the hot blast temperature of th...