• Journal of Power System Engineering
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• v.3 no.2
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• pp.34-41
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• 1999

This paper identifies a linear combustion model of Refuse Incineration Plant(RIP) which characterizes its combustion dynamics, where the proposed model has thirteen-inputs and one-output. The structure of the RIP model is given as an ARX model which obtained from the theoretical analysis. And then, some unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. In result, it is shown that the proposed model well approximates the input-output combustion characteristics riven by experimental data sets.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.3
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• pp.354-362
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• 2000

This paper identifies a discrete-time linear combustion model of Refuse Incineration Plant(RIP) which characterizes steam generation quantity, where the RIP is considered as a MIMO system with thirteen-inputs and one-output. The structure of RIP model is described as an ARX model which are analytically obtained from the combustion dynamics. Furthermore, using the Instrumental Variable(IV) identification algorithm, model structure and unknown parameters are identified from experimental input-output data sets, In result, it is shown that the identified ARX model well approximates the input-output combustion characteristics given by experimental data sets.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.63-68
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• 2017

This paper reviews the learning model of a forward slip ratio in order to improve the mass-flow stability and strip qualities in the hot strip finishing mill process. Firstly, it is shown, from mathematical analysis, that the significant parameters of the forward slip ratio are the tension, looper angle, and roll velocity. Secondly, a discrete-time learning model of the forward slip ratio is proposed from these parameters, which is identified by an instrumental variable (IV) identification algorithm. Finally, it is shown from computer simulation that the proposed learning model is more effective than the existing learning model.