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Sintering Heat Treatment Process Modeling and Control Based on Data-drive

Author: YingYuQian
Tutor: ChenJinShui；LuJianGang
School: Zhejiang University
Course: Control theory and control engineering
Keywords: Sintering PIDNN Intelligent Control Multi- model fuzzy weighted Fuzzy Control
CLC: TP273
Type: Master's thesis
Year: 2012
Downloads: 34
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Abstract


The Sintering production is an important pre-processing, blast furnace feed to be able to minimize fluctuations in the blast furnace feedstock to provide a reliable guarantee for the smooth production of blast furnace. With the rise of the price of iron ore and enhanced energy saving and environmental protection awareness of the whole society, improved technology for energy saving in the iron and steel industry, expand imperative. As an important part of the blast furnace production and quality, the sintering process technology improvements in the entire ironmaking process energy saving technology improvements essential. Therefore, the study of the sintering process has important theoretical and practical significance. Sintered ignition process control problem and sintering process modeling of the thermal state control, PIDNN (Proportional-Integral-Derivative Neural Network) control point stove temperature, proposed the establishment of the research unit idea of ??time-series data, the use of multi-model fuzzy weighted prediction of the thermal state of the sintering process modeling using fuzzy control method to control the state of the sintering heat. Main work and contributions of this paper are as follows: (1) PIDNN smart control algorithm to control the the sintering point stove oven temperature, in the case of frequent fluctuations in the ignition furnace gas negative pressure and heat value, self-learning function of neural network control system enables real-time tracking of scene objects will be charged and the appropriate control parameters accurately stable within the requirements. According to the characteristics of the field instrument PIDNN control method and expert system technology combined to achieve the goal of intelligent control sintering ignition furnace temperature, and the implementation of on-site during the sintering running in good condition. (2) to establish the idea of ??time series data research unit, using the idea of ??data-driven modeling sinter shop in Junction station car ignition start from the mixing of the ingredients, broken before cooling sinter heat treatment process, in accordance with the trolley travels The direction of the research unit of the sub-set, the recording of the whole process of data, including the ignition temperature, the thickness of the material layer, the negative pressure through the windbox and temperature. (3) multi-model fuzzy the weighted forecasting methods, the use of back-propagation neural network and generalized regression neural network independent of temperature prediction model identification, two models predict the output for the same set of input, the predicted results by fuzzy weighted new forecast output, in order to establish the sintering process forecasting model of the thermal state. (4) sintering machine ideal conditions run data recording, data research unit, multi-model fuzzy the weighted forecasting methods to establish ideal conditions bellows temperature prediction model forecast bellows temperature target, and then create fuzzy rules by controlling bellows negative pressure on the temperature of each bellows independent fuzzy control.

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