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Yul Yunazwin Nazaruddin,Abdullah Nur Aziz,Oktaf Priatna 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
The temperature of high-pressure steam is very important to be controlled in order to perform other processes safely, especially for boiler-turbine system. Typically, PID regulator with fixed parameter is used for that purpose. However this method may usually deteriorate the control performance, particularly if the systems exhibit highly coupled behaviour. This paper will present the integration of intelligent control technique, especially artificial neural network, to challenge some deficiencies of PID regulator in dealing with such problem. The proposed control algorithm consists of a neural network controller, which is implemented parallel to the PID controller. The presented neural network controller involves HP steam temperature and its set-point as input and error control signal as a learning signal to be minimized. The ability of proposed algorithm is tested through step-like load disturbance into boiler plant model. Remarkable results have been obtained during this disturbance test. These results showed better performance to reject the disturbances compare with the controller which involves PID regulator alone.
Yul Yunazwin Nazaruddin,Abdullah Nur Aziz,Wisnu Sudibjo 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In heat generation process, performance improvement is a critical factor and essential. An alternative solution is by designing an advanced combustion controller based on neural-predictive control strategy. However, for accomplishing such goal it requires adequate boiler model as well as combustion model. Although heat transfer and combustion processes in boiler are too complex to be analytically described with mathematical model, it can be approximated by artificial neural network model. This paper presents an alternative strategy to model the boiler and combustion process as well as proposes an advanced control strategy that takes the advantage of artificial neural network’ ability as a universal function approximation. A feedforward neural network algorithm is applied to construct the models and the gradient descent technique seeks the optimal network weights, from which the nonlinear predictive control law under the reduced excess air level is derived. Direct application of this control strategy to real-time data taken from a running boiler system at an oil refinery plant demonstrated the benefit of the algorithm to improve the boiler combustion performance.