Multi-target evaluation optimization method based on group decision making retrieval strategy

Abstract

The invention discloses a multi-target evaluation optimization method based on a group decision making retrieval strategy and a multi-target evaluation structure for the optimization calculation of set values. The method comprises the following steps of: defining an encoding function; randomly assigning weights for initialization; performing case retrieval after decoding; calculating individual adaptability; judging whether the accuracy meets requirements or not (outputting optimized weights if the accuracy meets the requirements, otherwise, performing the case retrieval again); obtaining final weights by adopting group decision making; calculating case similarity to obtain new retrieval results; judging whether new cases to be retrieved exist or not; calculating product quality, product yield and an energy consumption index within a unit time; evaluating the set values; judging whether to perform compensation and correction on the set values or not; and outputting final set values. The method and the structure are applied to control over the roasting process of a shaft furnace in the metallurgical industry; the rationality of the set values can be evaluated; the quality of roasted ore is improved by 2 percent, the yield of the roasted ore is improved by 4 percent, and energy consumption is reduced by 5 percent; and optimal target values of production indexes are realized, the quality of the roasted ore is improved, gas consumption in a production process is reduced, and the aims of energy saving and consumption reduction are fulfilled.

Description

technical field [0001] The invention relates to a multi-objective evaluation and optimization method, in particular to a multi-objective evaluation and optimization method based on a group decision-making retrieval strategy in the field of shaft furnace roasting process control in the metallurgical industry. technical background [0002] In the field of industrial process control, because the controlled object often has the characteristics of multi-variable, strong coupling, and strong nonlinearity, it is difficult to realize the optimal control of production indicators, resulting in low economic benefits. The decisive role in this is the reasonable operating value of the key controlled variable Not easy to get. The application of supervisory control has promoted the research on the setting method of key controlled variables in the industrial production process. The research goal is to ensure the optimization of production indicators through reasonable setting values, which ...

AI Technical Summary

Problems solved by technology

However, whether it is set by the mechanism method or by the intelligent method, in the control process, in order to ensure the rationality of the set values, it is still necessary to compensate and correct those unreasonable set values ​​through the actual control effect of the production index. , but the analysis and measurement process of production indicators is often time-consuming and laborious. From the perspective of control, this setting process has a large lag characteristic, and the real-time effect is not ideal, which makes it difficult to optimize the realization of production indicators.

Although there are also demonstrations of mixing the above methods, the core problem - how to optimize the set value in real time is still an unfinished task

[0005] It can be seen that when applying the case reasoning method, the distribution of case attribute weights must first be solved; when setting the model to run online, it is necessary to solve the evaluation problem of whether the set value is reasonable. If these problems are not solved, the rationality of the set value will be difficult It is not conducive to the optimization of many production indicators, such as energy consumption, quality, output, etc.

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