Diagnosis of the instability of the cooling behaviour of flat steel products through parametric characterisation, neural networks and statistics

Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips

“…Some researchers have successfully adopted ANNs to predict heat transfer coefficient, temperature or water flow [25][26][27]. Valentina introduced an ANN to find correlations between model parameters and process variables [28]. Xing developed a hybrid intelligent identification model by combining the RBF neural networks, CBR and fuzzy logic reasoning, which can make a great contribution in improving the coil temperature precision by prediction precision of correct identification [29].…”

Online Cooling System and Improved Similar Self-adaptive Strategy for Hot-rolled Seamless Steel Tube

“…In addition, there are also many artificial intelligence algorithms used to build precise temperature model and achieve fairly good preformation. Such as fuzzy control, 13,14) neural network, 15) and machine learning 16) etc. Despite of the different cooling mechanism, the methods mentioned above in plate and strip making are well worth giving a certain attention.…”

Research and Application of Model and Control Strategies for Hot Rolled Strip Cooling Process Based on Ultra-Fast Cooling System

“…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][7][8][9][10] such as fuzzy control model, neural network model and instant base learning by k-NN algorithm are uti- Ultra-fast cooling technology as an effective method for control microstructure and property, is widely used in hot rolled strips. For precise control of strip temperature in cooling process, a mathematical model based on UFC is established to calculate UFC-T and CT in high pressure mode, or only CT in low pressure mode.…”