Abstract: The invention belongs to the technical field of quality control of steel plates and strips products, and relates to a prediction method of crown of steel plates and strips based on data driving and mechanism model fusion. By establishing an outlet crown mechanism model of a hot continuous rolling, the mechanism model and a DNN model are combined to establish a DNN model for predicting crown of steel plates and strips, and the calculated value of the mechanism model is taken as a benchmark value of the outlet crown. The deviation amount between the benchmark value and the actual values of the outlet crown is taken as output of the DNN model for predicting crown of the steel plates and strips, and then sum of the predicted value and the benchmark value based on the DNN model for predicting the crown of the steel plates is taken as the final predicted value of the crown of the steel plates and strips.

Abstract: A combined logic control circuit and a sewage treatment system are provided. The combined logic control circuit includes: at least one signal input component, a control component and at least one signal output component; each signal input component transmits at least one path of first communication signal in a photoelectric isolated manner and converts the first communication signal into a second communication signal; the control component generates at least one path of first control signal according to the at least one path of second communication signal; and each signal output component processes one path of the first control signal to control at least one external electrical device, thereby implementing a particular circuit function; and the power switch component provides power supply to the various circuit components in a photoelectric isolated manner.

Abstract: The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.

Abstract: A combined logic control circuit and a sewage treatment system are provided. The combined logic control circuit includes: at least one signal input component, a control component and at least one signal output component; each signal input component transmits at least one path of first communication signal in a photoelectric isolated manner and converts the first communication signal into a second communication signal; the control component generates at least one path of first control signal according to the at least one path of second communication signal; and each signal output component processes one path of the first control signal to control at least one external electrical device, thereby implementing a particular circuit function; and the power switch component provides power supply to the various circuit components in a photoelectric isolated manner.

Abstract: The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.