For this reason, a post-calculation is performed. It gathers measurements and observes the process. Its main task is to carry out an online adaptation, i.e. control errors are compensated by continuously adapting the models to the present technical process. Under theoretical aspects, neural networks have been shown to be the best possible approximators of relations within process data. Today, neural networks contribute favorably to both modeling and adaptation in rolling mill automation.
Neural networks improve the overall model accuracy around setpoints where process data is available.
They are the key to fit the general physical models to the needs of the automation of a specific mill. Knowledge gathered by process engineers doing analytic modeling has not become obsolete, but plays an important role in the hybrid system as source of the analytic system component. The analytic model is the basis of the complete model. It allows for operation in novel process states and for interpretation of results. Online adaptation is best performed solely by the neural network within the combined system.