Prediction System of Burning Through Point (BTP) Based on Adaptive Pattern Clustering and Feature Map

Cheng, Wushan

doi:10.1109/icmlc.2006.258372

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…Thus, in [9], an adaptive genetic algorithm was used to control BTP in the iron ore sintering process, where the input layer of the NN is parameters of the initial material, density, strand speed, and ignition temperature, and the output layer is the values of temperature and underpressure of sintering gases and gases in wind boxes. The genetic NN was used also for the BTP prediction of iron ore sintering in [10], where 707 groups of data were given to the input after clustering and classification of temperature and underpressure vectors from 18 wind boxes. The adaptive structural clustering system is based on a spatial clustering of initial data, a self-organizing NN map for extracting data relevancy properties, and a Kohonen map for the learning network.…”

Section: Introductionmentioning

confidence: 99%

Application of grey system theory to phosphorite sinter process: From modeling to control

Toktassynova

Fourati

Suleimenov

2020

Asian Journal of Control

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The sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of phosphorite ore sintering is to regulate the parameters of the process to obtain a high quality sinter. The parameter clearly responsible for sinter quality is the temperature in the wind box. Therefore, in order to solve the control task, it is necessary to predict the highest temperature of the charge (also known as the burn through point (BTP)). In this paper, the theory of grey systems is used as a predictive model, which makes it possible to obtain an adequate model that uses a small number of initial samples of real temperature data. Based on the grey model GMC(1,n) a new optimal model is presented, which is constructed by using optimization algorithm. Optimal model predicts the BTP, and to establish an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the “particle swarm” algorithm.

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Section: Introductionmentioning

confidence: 99%