Controlling and improving quality of the fertiliser production process using neural network models

Fertiliser production process is characterised by being a dynamic process which is not easy to be predicted and controlled due to uncertain, imprecise and vague parameters' relations. Although mathematical modelling techniques are very well developed, these types of dynamic processes are difficult to be modelled by those techniques and also the regression models are complex to be used for real time control and, usually, their errors are significant. The main and most important quality characteristic in the fertiliser production process is the moisture content. This parameter affects the product shelf life, effectiveness and harmful internal reactions. In this research, two different artificial neural network (ANN) approaches are developed to predict the moisture content of the produced fertiliser: the back-propagation multilayer perceptron (BPMLP) and the radial basic function (RBF) nets. The two models performed satisfactory in predicting the moisture content with low error percent. Predicting the moisture content, the quality of the produced fertiliser can be enhanced either by reheating, adding chemicals, or both.