Modeling of Real-Time Double Loops System in Predicting Sintering’s BTP

Cheng, Wushan

doi:10.1007/978-3-642-15853-7_3

Cited by 9 publications

(1 citation statement)

References 3 publications

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“…An intelligent control method based on a hierarchical configuration is researched in Cao et al (2010). To handle the large time delay in sintering process, predictive control methods are now widely used for BTP control (Cheng, 2010; Greenwell and Vahidi, 2010; Kouro et al, 2009; Lu and Tsai, 2007; Nolde and Morari, 2010; Wu and Xu, 2007). There are also some other techniques to be used in modeling sintering process such as BPNN (back propagation neural network), RBF (radial basis function), SVM (support vector machine) and fuzzy network and ELM (extreme learning machine) (Feng et al, 2012; Huang et al, 2006; Li et al, 2016; Meng et al, 2012; Mengjoo et al, 2000; Wang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new pattern based Petri net to model sintering production process

LIU

2017

Transactions of the Institute of Measurement and Control

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In this paper, a new Petri net model based on pattern recognition method is presented for describing a certain stochastic hybrid systems. The application of this method concerns sintering production process. The main idea consists in describing the variation of patterns over time through the so-called ‘pattern class variable’ rather than state variable or output variable. A new petri net control model is constructed based on pattern class variable. The marks are defined as posterior probability of the cluster. By redefining the marks and transition on the basis of ordinary Petri nets, it can represent any combination of discrete-event and continuous element and has probability property. The simulation results are provided based on real plant data to illustrate the effectiveness of the proposed approach. This method might provide the satisfied results for the practical applications without having the exact mathematical models.

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