Application of neural networks to temperature control in thermal power plants

In a thermal power plant with once-through boilers, it is important to control the temperature at the middle point where water becomes steam. However, there are many problems in the design of such a control system, due to a long system response delay, dead-zone and saturation of the actuator mechanisms, uncertainties in the system model and/or parameters, and process noise. To overcome these problems, an adaptive controller has been designed using neural networks, and tested extensively via simulations.

One of the key problems in designing such a controller is to develop an efficient training algorithm. Neural networks are usually trained using the output errors of the network, instead of using the output errors of the controlled plant. However, when a neural network is used to control a plant directly, the output errors of the network are unknown, since the desired control actions are unknown. This paper proposes a simple training algorithm for a class of nonlinear systems, which enables the neural network to be trained with the output errors of the controlled plant. The only a priori knowledge of the controlled plant is the direction of its output response. Due to its simple structure and algorithm, and good performance, the proposed controller has high potential for handling difficult problems in process-control systems.