What is it about?
The article is about using artificial neural networks (ANN) to accurately predict how well 1,2,4-triazole compounds prevent metal corrosion. ANN models performed better than traditional methods, helping to quickly find effective, eco-friendly corrosion inhibitors.
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Why is it important?
It’s important because corrosion damages pipelines, machines, and structures, leading to pollution, safety risks, and huge financial losses. By using ANN models to predict corrosion protection quickly and accurately, industries can find better, eco-friendly inhibitors faster, saving money, protecting the environment, and improving safety.
Perspectives
The study opens important future perspectives: Faster discovery: ANN models can speed up finding new, greener corrosion inhibitors without costly experiments. Wider applications: This method could be expanded to predict inhibitors for other metals and harsh environments. Real-time monitoring: Future systems could use ANN models to predict corrosion in real industrial settings, allowing early warnings and smart maintenance. Better materials design: Combining ANN with new quantum calculations could help design custom-made inhibitors with higher performance.
Mr Ramzi T.T Jalgham
Bani Waleed university (Bani Walid)
Read the Original
This page is a summary of: Precise Prediction on the Corrosion Prevention Ability of 1,2,4- Triazole Derivatives: An artificial Neural Network Approach, ES Materials & Manufacturing, January 2024, Engineered Science Publisher,
DOI: 10.30919/esee1344.
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