Development of Numerical Modeling and Temperature Controller Optimization for Internal Heating Vacuum Furnace

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.