Development of a Waste-Heat Boiler Model Based on Recurrent Neural Networks

The work is devoted to the development of a dynamic model of a waste heat boiler based on a recurrent neural network. The object of modeling is presented as a complex thermodynamic system. The dynamic processes taking place inside the boiler are non-linear and interconnected. Changes in the technological parameters of the waste gases occur in ranges that do not allow to obtain an acceptable quality of the linearized model. Due of the difficulty of creating a mathematical description that takes into account the operation of the installation in different modes, recurrent neural networks were chosen to implement the simulation task. A technique was developed for the synthesis of a neural network model. As a result of the application of the technique, a neural network model was synthesized that describes the change in the technological parameters of the waste heat boiler in the "Power boost" "Rated Load", "Power reduction" operating modes. The model output is the temperature of the network water behind the boiler. The created model takes into account the change in the water flow through the boiler, the change in the inlet water temperature, the increase and decrease in the temperature and pressure of the waste gas at the inlet of the waste heat boiler. In the formation of training and test samples for the neural network, archival trends obtained during the operation of the waste heat boiler were used.