DIYALA JOURNAL OF ENGINEERING SCIENCES

Abstract

Scale is a term generally used in industry refers to any deposit on equipment surface. Usually the deposition of scale is undesirable because it is uncontrolled and a build-up of scale on metal surfaces may act as insulation causing decreased efficiency. So removal of scale has gained special attention in the last few years due to its significance, when predicting removal scale weight. However, the complexity and variability makes it hard to model its effects. This study evaluates the usefulness of Artificial Neural Networks (ANN) to predict the scale removal weight as a function of several of their properties which have been related in previous studies i.e. time, concentration of organic acid salts, Temperature, density, viscosity. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis. The structure of ANN models is [5-18-1] the best because reach MSE 0.001 with AARE%, S.D%, and R (0.12, 0.46, 0.9) respectively. The training of network use MATLAB program.