The Research Of Typical Strip Heat Treatment Process Optimization And Control

The accuracy of strip heat treatment can be greatly improved by optimizing the temperature profile of heat treatment and constantly controlling the temperature rise and drop by means of applying mathematical and intelligent model-based methodology,and the excellent product performance can be obtained as well.Heat treatment with high accuracy,due to its capability to fulfill alloy contents reduction and product customization in metallurgical processes,is well recognized in metallurgical industry and starts to be used in strip production process.Restricted to the current conditions such as fluctuation of coming material,equipment capacity and detection devices,there are many problems to be solved to achieve accurate strip heat treatment by process optimization and control,and more researches should be carried out especially in modeling and process control.Therefore,this thesis aims at study of the process optimization and control during strip heat treatment by applying mathematical and intelligent model-based methodology,which is of utmost importance to realize green and intelligent metallurgical industry.The typical heat treatment of strip production involves strip hot dip galvanizing annealing furnace and laminar cooling.Focused on the critical points and issues this research was carried out in the following aspects:first,the annealing temperature optimization method,strip temperature control in heating and cooling section were systematically studied during hot dip galvanizing continuous annealing process;second,further to the relatively unrepresentative annealing cooling process in temperature control,cooling path control in laminar cooling process after hot rolling was investigated.The research results proved to be effective based on verification and application in modern hot dip galvanizing line and hot rolling line.The main research results and innovations:1.The method of annealing temperature setting and results assessment by data mining method was proposed.Based on which,annealing temperature intelligent setting was achieved and the annealing temperature can be reduced by 0-30? for 85.39%of the experimental strip.2.Temperature observer was employed to track the temperature of strip at any point of the heat treatment device,and it is used for both temperature profile control in annealing furnace and laminar cooling control.3.Section by section strip temperature control method based on golden section was proposed.It was used for combustion air flow setting of NOF section and temperature setting of RTF section,by which,the annealing temperature fluctuation deviation could be controlled within ±2?.4.Quadratic programming method for cooling path control was proposed,which achieved the laminar cooling path control and made the hitting rate of coiling temperature deviation within ±15? up to 97.27%.The detailed research work is as follows:(1)The annealing temperature setting strategy based on data mining methods and its accuracy online assessment method were established.In the proposed method,the initial annealing process of a typical steel grade was derived by the hot dip simulator.Then based on the real-time data in real production and test data in laboratory,the IBK algorithm was used to optimize annealing temperature,and neural network algorithm was used to evaluate the optimized annealing temperature online.(2)The optimization and control strategy of annealing temperature based on temperature observer was developed Firstly,one dimensional unsteady heat conduction model ignoring the phase transformation was established,coupling the heat transfer model through boundary conditions.In PH-NOF,the convection flux between the strip and furnace gas was calculated by experience criterion equation,while the strip radiation flux was obtained by equivalent circuit.In RTF,the imaginary plane equivalent emissivity method combined with equivalent circuit was used.Secondly,the temperature observer was established on the basis of the physical models,and parameters of the observer were modified based on the nonlinear least square optimization method.At last,based on the observer,the optimization strategy employed the combustion air flow in PH-NOF and furnace temperature in RTF to control strip temperature during the annealing process.(3)The cooling path control method based on observer was derived.First,the enthalpy model was established,in which,strip enthalpy was calculated by regular solution sub-lattice model,austenite transformation was calculated by C diffusion-controlled phase transformation model and the boundary conditions for laminar cooling and gas jet cooling were based on empirical formula.On this basis,temperature observer and its parameter optimization method were developed.Finally,based on the observer,the cooling path control problem was transferred into two quadratic programming problems with constraints,and the best control rate was obtained by solving the constrained optimization problem.