Author Description

The aim of COGNITVE CONTROL was to create cognitive automation systems with the capabilities automatic control performance monitoring (CPM), self-detection and automatic diagnosis of faults (sensors, actuators, controller) and self-adaptation in control system environments to optimise the product quality and minimise energy consumption in steel during the whole life cycle. In this project several software tools for online Control Performance Monitoring (CPM), monitoring energy efficiency, diagnosis of poor performance root-causes and control re-tuning for univariable and multivariable, linear and nonlinear processes were developed. The software tools were Graphical User Interface (GUI) that provided interface to access process data. The implemented methodologies were subsequently published as conference and journal papers. The methods were tested at hot strip mills, annealing furnaces and galvanizing lines.