Improving the performance of industrial boiler using artificial neural network modeling and advanced combustion control

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 networkpsilas 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.