Lazy learning-based self-adaptive robustness forecast control method of blast furnace molten iron quality

Abstract

The invention provides a lazy learning-based self-adaptive robustness forecast control method of blast furnace molten iron quality, and relates to the technical field of automatic control of blast furnace melting. The lazy learning-based self-adaptive robustness forecast control method comprises the steps of determining controlled quality and control quantity; acquiring historical production input-output measurement data of a blast furnace to construct an initial database; constructing an inquiry regression vector, and determining abnormal data; inquiring a similar leaning subset from the database, selecting an optimal learning subset, and processing the abnormal data; building a forecast model by taking the optimal learning subset; calculating index reference track of the molten iron quality, constructing a forecast control performance index to obtain an optimal control vector; and sending the optimal control vector to a bottom-layer PLC system, adjusting an execution mechanism, acquiring a new group of blast furnace measurement data, pre-processing the data, and updating the database. According to the method provided by the invention, the influence of the input-output interference can be effectively suppressed, the influence of the abnormal data is overcome, the blast furnace molten iron quality is stabilized to a value near to an expected value, and stable running, good quality and high yield of the blast furnace are facilitated.

Description

technical field [0001] The invention relates to the technical field of automatic control of blast furnace smelting, in particular to an adaptive robust predictive control method for blast furnace molten iron quality based on lazy learning. Background technique [0002] Blast furnace ironmaking, as the most important ironmaking method, is developing in the direction of large-scale, high efficiency, low energy consumption, and automation. The closed-loop automatic control of blast furnace ironmaking has always been a difficult problem in the field of metallurgical engineering and automation. Since the blast furnace ironmaking system is a complex physical and chemical reaction, multi-phase, multi-field coupling nonlinear, large lag, and dynamic time-varying system, it is difficult to establish an accurate mathematical model for it, and it is difficult to achieve stable control. At present, the indicators that are widely used to indirectly reflect the internal state of the blast...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a method for adaptive robust predictive control of blast furnace molten iron quality based on lazy learning, which effectively solves the problem of online update of the predictive model in nonlinear predictive control, and This method can reuse useful data samples, which greatly improves the utilization rate of offline and online input and output measurement data, can effectively suppress the influence of abnormal data on the controller, enhance the robustness of the controller, and thus improve the stability of the blast furnace ironmaking system sex

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