Neural Network Based Model of an Industrial Oil-Fired Boiler System

Abstract

In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant. A two-layer feed-forward neural network with Hyperbolic tangent sigmoid transfer function (Tansigmoid) and linear output neurons (Purelin) are used at the hidden and output layer respectively to fit the neural network model. The neural network model is then trained off-line with Levenberg-Marquardt Back Propagation Algorithm (trainlm). The neural network model when subjected to test, using the validation input data; shows that the simulated model outputs for both temperature and pressure agree closely with the actual plant outputs, with regression value of 0.97. Furthermore, Proportional Integral Derivative (PID) Controller is used to control the neural network model. Simulation studies results indicate the effectiveness of the developed technique.