Abstract

Modelling of the effect of newly developed mild steel (MS) corrosion inhibitor in Iraq was investigated using artificial neural network (ANN) and Response Surface Methodology Design of Experiment (RSM-DOE) methods. The most significant parameters among the parameters studied and the optimum coating conditions was also investigated. Weight loss method (WLM) as well as Scanning electron microscope (SEM) were used in the experimental work to obtain data for modelling.

The inhibitor used was made in the center’s laboratories called N-(3-Nitrobenzylidene)-2-aminobenzothiazole. The MS specimens were tested for different immersion times and corrosive solution temperatures. Different concentrations of the inhibitor from of 0, to 1000 mg/L were used in the study.

The results showed that within the concentrations studied, the corrosion inhibition performance increased with increasing N-(3-Nitrobenzylidene)-2-aminobenzothiazole concentration. The ANN model proposed with the Gaussian activation function was accurate for both testing and validation up to 99%. The RSM method used indicated that comparing time and concentration alone, inhibitor concentration was more significant than the immersion time in the corrosive solution. On the other hand, the effect of temperature and time were opposite to one another.

While increasing time of immersion increased corrosion rate, temperature effect was the opposite.

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