LAUSR

Process temperature is an important process parameter for hot-rolled strips, directly affecting microstructure and property. Recently, ultra-fast cooling (UFC) technology is widely used in development and production of hot rolled strips, because of its high cooling rate, which can reduce alloy element and improve microstructure and property.1,2) In order to get ideal microstructure of hot rolled strips, UFC and laminar cooling on run out table are jointly utilized to cool hot strips from FDT (Finishing Delivery Temperature) down to UFC-T (UFC delivery temperature) and CT (coil temperature) by setting process path. In addition, UFC can be used individually, by which cools strip to UFC-T, and then to CT in air condition. Therefore, precisely and highly flexible control of UFC-T and CT in cooling section is extremely important. To achieve precise control of UFC-T and CT on run out table, many models and strategies are adopted. Physical model can achieve accuracy temperature control by describing heat transfer process. But for convenience industrial applications, the physical model is usually simplified.3,4) Statistical models have been developed to solve the situation that closely reflects reality as much accurately as possible.5) And there are also many artificial intelligence algorithm models,6–10) such as fuzzy control model, neural network model and instant base learning by k-NN algorithm are utiResearch and Application of Model and Control Strategies for Hot Rolled Strip Cooling Process Based on Ultra-Fast Cooling System