Aiming at the control of high-power electric arc furnace in metallurgical industry, an intelligent integrated control scheme based on fuzzy control neural network and multi-objective optimization decision-making is proposed. Firstly, the variable structure fuzzy neural network control is used to design the temperature outer loop controller, and the current command signal is provided to the three-phase electrode current balance inner loop. Then, various optimization targets are integrated in the inner loop control, the optimization objective function is constructed, and multi-objective blur is used. Optimize decisions to achieve a balance across the system.
Domestic and foreign scholars have done a lot of work on the control object with the characteristic large delay characteristics by using the electrode lift to automatically control the furnace temperature, in order to achieve the goal of low energy consumption and high power factor, because the electrode control of the electric arc furnace is more In the variable system, there is always a certain degree of correlation between the controlled quantities. Therefore, the electric arc furnace system is not easy to establish an accurate mathematical model, and it faces the problem of nonlinear large delay time-varying and strong mismatch, and adopts conventional control. It is difficult to complete effective control of production.
The fuzzy control method based on fuzzy theory, using fuzzy language and rules to describe the dynamic characteristics and performance indicators of a system, can effectively overcome the impact of pure lag and parameter changes on the system, and it is easy to achieve nonlinear and strong nonlinearity with uncertainty. The combined object is controlled, and has strong robustness and anti-interference ability. Another neural network-based intelligent control method has powerful learning ability and direct processing capability of quantitative data. Offline training and online through the network. Learning, the controller has the ability to self-adjust and adapt, further improving the effect of real-time control.
To this end, the author adopts an intelligent integrated control scheme based on fuzzy control neural network and multi-objective optimization decision-making, which realizes the arc furnace temperature and three-phase electrode by the double closed loop structure of temperature control outer loop and current balance control inner loop. Automatic control of current balance.
The temperature control outer ring adopts a variable structure fuzzy neural network controller to provide the best electrode current command signal current balance control inner ring according to the actual temperature and temperature change trend information of the electric arc furnace to command the current fast tracking and The electrode current fluctuation is the minimum target, and the energy loss is comprehensively considered. The multi-objective fuzzy optimization decision method is used to realize the balance control of the three-phase electrode current.
The author uses a layer feedforward network to implement the neural network structure in the controller. In order to solve the problem that the fuzzy rules in the fuzzy logic control are not suitable for the controlled process change, the variable structure fuzzy neural network control is adopted to increase the self-learning self-adaptive ability of the system. Variable structure fuzzy neural network control in the process of neural network learning to change the hidden layer nodes can improve the convergence speed of the neural network, and effectively avoid the neural network trapping the local extremum hidden layer nodes from a small to many variable structure process It is a learning process in which fuzzy rules are from coarse to fine.
Mathematical model of multi-objective fuzzy optimization decision control In multi-objective systems, each target strives to achieve optimality, but because a certain target change may lead to the reverse change of other targets, the system multi-objective fuzzy optimization decision is to find A feasible solution or satisfactory solution that does not deteriorate one goal and makes other goals better. The basic principle is to first find the optimal value of the constraints of each single target, then fuzzify each optimal value, and finally find the solution that can make the membership function of each fuzzy optimal value intersection take the maximum value, which is the multi-objective problem. Optimize the solution.
The electrode current balance control inner ring aims at the fast tracking of the command current and the minimum electrode fluctuation, and comprehensively considers the requirements of the process and energy saving. Therefore, the inner ring of the high-power electric arc furnace temperature control is also a multi-objective information and control volume system, which is optimized. The goal is as follows, the imbalance between the three-phase currents reaches the equilibrium point of the adjustment time energy loss. The control rate is solved by the fuzzy language model for the three-phase electrode system with the optimized objective function. The fuzzy language model is used to form the fuzzy target minimum set pair and the fuzzy target minimum set is used to construct the fuzzy decision function.
Among them, the weighting coefficient satisfies the genus two, so that the multi-objective problem of the system is transformed into a single-objective general plan to solve the problem, and the optimal electrode motion control command is obtained to make the application of the intelligent integration control of the most timely Li Zhengguo in the high-power electric arc furnace system. The judgment is a fight.
Conclusion Push or pull production can produce different system performance. Specifically, if a company uses a push system, it will lead to an increase in the inventory of in-process products and a disorder in the production of energy and processes, while maximizing the systemic effects of increased output rates and resource equipment usage. The pull system can minimize the effect of inventory and accumulation of products in the system, reduce buffer space, but also relatively low utilization rate of low output and equipment resources. In specific production practices, enterprises can fully consider the advantages and disadvantages of the two types of logistics control according to their own characteristics, and integrate the system operation of push or pull production to achieve the purpose of improving efficiency and reducing costs.
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