A multivariate molten iron quality soft sensor method based on robust random weight neural network

Abstract

A multi-element molten iron quality soft measurement method based on a robust random weight neural network belongs to the field of blast furnace smelting automation control, especially a multi-element molten iron quality based on Cauchy distribution weighted M-estimation random weight neural networks (M‑RVFLNs). Parametric dynamic soft measurement method. This invention uses the principal component analysis (PCA) method to screen out the most important parameters that affect the quality of molten iron in the blast furnace as model input variables, and constructs a multivariate dynamic prediction model of molten iron quality that has an output self-feedback structure and takes into account input and output data at different times. It can simultaneously Multivariate dynamic soft measurement is performed on the main parameters Si content, P content, S content and molten iron temperature that characterize the quality of blast furnace hot metal. The invention includes the following steps: (1) auxiliary variable selection and model input variable determination; (2) training and use of M-RVFLNs soft measurement model.

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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Problems solved by technology

This method solves the problems that the temperature detection of blast furnace molten iron requires manual participation, intermittent and discontinuous, many consumables, and unstable temperature measurement values.

[0006] The method reported in the above-mentioned patents and similar methods related to other relevant literatures are only for forecasting and soft measurement of a single molten iron quality element (such as Si content, S content, molten iron temperature, etc.), and fail to determine the main parameter that characterizes the quality of blast furnace molten iron, namely Si (Silicon) content, P (phosphorus) content, S (sulfur) content and molten iron temperature are multivariate online forecast at the same time, so it cannot fully reflect the quality level of molten iron, and its practicability is poor

At the same time, because these methods do not consider the input and output timing and the time-delay relationship in the process, the established static model cannot well reflect the inherent dynamic characteristics of the blast furnace smelting process

In addition, in the actual ironmaking production process, due to the failure of detection instruments and transmitters and other abnormal interference, there are often outliers in the measurement data

These methods mainly consider the soft measurement of molten iron quality parameters under ideal furnace conditions, and their robustness is poor. When disturbed by outliers, these methods cannot suppress the interference of outliers and measure the quality parameters of molten iron more accurately.

To sum up, there is currently no multivariate dynamic robust soft-sensing method for hot metal quality parameters (Si content, P content, S content, and hot metal temperature) in the blast furnace smelting process at home and abroad.

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