Integrated process control and diagnostics system for hot rolling mills based on evaluated physical data in comparison with mathematical process-models by using artificial intelligence methods

IPCDS

The project aimed at improving the product quality as well as reducing the downtime of rolling mills by developing a generic process control and diagnostic system. Bringing down the mill downtime to a minimum have consequently made it possible to reduce energy consumption.

The application of mathematical models and Artificial Intelligence (AI) techniques have eliminated the need for performing repeated laboratory and field experiments, thus helping to save resources. Employing automated process control and diagnostic systems result in improving the working condition inside the mill, which is of significance due to the warm and moist environment prevailing inside rolling mills. Improving the accuracy of model estimates of parameters like temperature, thickness, width, pressure, spread etc. enable meeting the customer requirements in a better manner.

Start Year: 
2003
End Year: 
2006
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