Abstract
The present study developed an intelligent model to predict the corrosion and scaling potential (CSP) of industrial cooling water circuits using artificial intelligence (AI) techniques. AI techniques have attracted a lot of attention due to the high accuracy and speed of calculations, as well as possible analysis of large datasets. The study analyzed nine years of data on cooling water quality parameters, including pH, alkalinity, hardness, dissolved solids, chloride, turbidity, suspended solids, and iron, from electric arc furnaces at the Khuzestan Steel Company. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), and multi-layer perceptron neural network (ANN-MLP) models were applied to predict CSP. The ANN-MLP model achieved the best performance with an R2 of 0.75, Mean Absolute Error of 0.34, and Mean Squared Error of 0.35, demonstrating that neural networks can effectively predict CSP in industrial cooling water. The results also showed that total hardness and chloride have the greatest impact on CSP in the circulating water circuits.
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