21 November 1995 Knowledge-based adaptive neural control of drum level in a boiler system
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Proceedings Volume 2596, Modeling, Simulation, and Control Technologies for Manufacturing; (1995); doi: 10.1117/12.227213
Event: Photonics East '95, 1995, Philadelphia, PA, United States
Abstract
A boiler system is an integral component of a thermal power plant, and control of the water level in the drum of the boiler system is a critical operational consideration. For the drum level control, a 3-element proportional-integral-derivative (PID) control is a popular conventional approach. This scheme works satisfactorily in the absence of any process disturbances. However, when there are significant process disturbances, the 3-element PID control scheme does not perform well because of lack of knowledge of proper controller gains to cope with such disturbances. Inevitably over time and use, PID controllers get detuned. Hence, there is good motivation to investigate alternatives to this control scheme. Multivariable control of drum boiler systems has been studied by many researchers. However, these approaches assume some process model equations (to a more or less extent) to design a controller. This paper presents a model-free approach in the sense that no plant equations are assumed. Only data is used to gain knowledge about the process, and the performance of the existing PID control scheme is observed. Based on this process knowledge, an intelligent control technique is developed, (artificial) neural network control (NNC). The technique proposed in this paper was tested on a process simulator. This paper shows that an intelligent control scheme such as NNC gives better performance in rejecting process disturbances when compared to 3-element PID control scheme.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nishith Tripathi, Michael Tran, Hugh VanLandingham, "Knowledge-based adaptive neural control of drum level in a boiler system", Proc. SPIE 2596, Modeling, Simulation, and Control Technologies for Manufacturing, (21 November 1995); doi: 10.1117/12.227213; https://doi.org/10.1117/12.227213
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Surface plasmons

Control systems

Digital micromirror devices

Neurons

Adaptive control

Process modeling

Autoregressive models

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