Control technology of annealing furnace

New development of annealing furnace control technology

The intelligent control technology is especially suitable for the control system with nonlinear and time-varying characteristics such as annealing furnace. With the continuous development of intelligent technology, more and more intelligent technologies are integrated into the control theory, such as system control, fuzzy control, neural network control, genetic algorithm, artificial immune and other control algorithms. These control methods are gradually applied in the control of industrial furnaces such as annealing furnace.

The main parts of annealing furnace are heating parts and cooling parts, which are expected to achieve relatively stable temperature control. Many scholars at home and abroad have done a lot of research in temperature control. The research results in control methods and control means directly promote the development of annealing furnace control.

The control system of the bell type bright annealing furnace is analyzed and studied. The algorithm is used to program. The PLC is applied in the electric control system of the furnace, and good production benefit is obtained.

A nonlinear multivariable model predictive split range transition control method is applied to the annealing production control technology, and a new annealing process control method is proposed. The process of production is optimized by nonlinear predictive control. The off-line optimal trajectory of the controlled object is applied to the controller. Through feedback control, some real-time parameters of the controller are compensated, and through online rolling optimization, the actual input and output curves are as close to the given ideal curves as possible.

The immune genetic algorithm is introduced into the furnace temperature control of the vacuum annealing furnace, and the parameters of the controller are adjusted online by using the immune genetic algorithm, so that the controller can meet the needs of temperature control for different kinds of workpieces. Practical application shows that this method can effectively reduce the return time of workpieces, reduce the scrap rate and greatly improve the production efficiency.

Particle swarm optimization algorithm is introduced into the temperature control of continuous annealing furnace. The parameters of the model are identified by particle swarm optimization algorithm, and then the identified model is applied to the model predictive control. Finally, the temperature control of the strip steel at the exit of the heating section of continuous annealing furnace is realized.

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