Abstract
Compared with traditional fire refining to remove impurities, the vacuum distillation method has the advantages of simple steps, high direct yield and no pollution to environment. The vacuum furnace, as the main equipment of vacuum distillation method, is widely used in resource regeneration and new material fabrication. To solve the engineering problems that the temperature control system of internal heating vacuum furnace has large time delay and real time temperature is hard to be measured precisely, the paper proposes an approach to model and calculate the temperature values for each node in the furnace at no-load. A disturbance model for the temperature field of the system by the vacuum furnace itself is developed. This methodology is useful for modeling the temperature field and the temperature control when implementing the experiment such as the vacuum reduction of MgO or the vacuum evaporation of Pb-Sn alloy using this furnace. To verify the effectiveness of the proposed method, experimental equipment is set up and experiments are done on the heating process of the internal heating vacuum furnace. Compared with the simulation results, the experimental results verify the correctness of the numerical modeling approach. In addition, a hybrid controller with Smith’s predictive proportion integral differential (PID) based on particle swarm optimization (PSO) algorithm is developed. The simulation results show that the controller has the advantages of no overshoot, short rise time and adjustment time compared with the conventional controller. It solves the pure delay for this temperature control model effectively.
Highlights
The internal heating vacuum furnace is one of the main equip ment of the vacuum distillation method, which has wide use i n the regeneration of secondary resources, extraction of preci ous metals from binary alloys, and development of new mate rials [1],[2]
The temperature in the fu rnace is difficult to measure when it is in operating mode, and the temperature control model has the characteristics of mod el uncertainty and serious time-delay [3],[4]
The main drawback for traditional proportion integral differential (PID) tuning is that the overshoot is relatively large, the response speed is slower, the self-adaptive ability is poor, and the control effect is still not ideal. Due to these characteristics, the output lags behind input, and it is impossible to completely follow the input change. Aiming to solve these problems, we developed a hybrid controller with Smith's predictive PID based on particle swarm optimization (PSO) algorithm
Summary
The internal heating vacuum furnace is one of the main equip ment of the vacuum distillation method, which has wide use i n the regeneration of secondary resources, extraction of preci ous metals from binary alloys, and development of new mate rials [1],[2]. The purp ose of this method is to speed up the change of c1 and c2 at e arly time so that the algorithm can get to the local search faste r, and the experimental results prove that the method is feasib le These methods lack diversity and tend to conver ge on local extremes too early, and the optimization effect is not obvious. (3) Design PSO-based hybrid controllers and explore varia ble learning factors of PSO for local models to improve the o verall performance.
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