Optimal Sensor Placement For Vertical Quench Furnace Based On Particle Swarm Optimization Algorithm

Large-scale vertical quench furnace is a kind of critical equipment for thermal treatment of high-strength aluminum alloy components, of which the temperature distribution has the characteristics of distributed parameter and non-homogeneity. For the reason that different number and position of the sensors configuration have a big impact on the quench furnace temperature’s control accuracy and uniformity, it is necessary to effective configure the quench furnace’s sensor in order to ensure the quench furnace temperature’s control accuracy and uniformity. Currently, researches on optimal sensor placement for Vertical Quench Furnace usually deal with position problem, neglect number problem. This paper has studied on the number and the position optimization of sensor, and proposed a comprehensive optimization strategy of the number and location of sensors.In this paper, research contents and innovations include the following aspects:1. The quenching process and structure of quench furnace is introduced and has builded a dynamic control model described by partial differential equation of quench furnace temperature distributed parameter system based on simplify the quench furnace model according to the main characteristics of quench furnace.2. A Chaotic Adaptive Particle Swarm Optimization algorithm (CAPSO) is proposed aiming at the problem of Particle Swarm algorithm is easy to fall into local optimal solution. The CAPSO algorithm is embedded with chaos optimization in the optimization process and adaptive the inertia weight according to the particle fitness value. Test results of comparison between CAPSO algorithm and the standard PSO algorithm through test function show that the CAPSO algorithm is better than the standard PSO algorithm in optimal accuracy and convergence rate.3. A sensor coverage model relating to the structure of quench furnace and temperature infomation is builded and is uesd as the fitness function. Calculate the sensor coverage change using different number of sensor based on CAPSO algorithm and get the number of quench furnace sensors by analyse the results. A Fisher information matrix associated with the position of sensors is structured with the standard of minimum parameter estimation error according to the quench furnace control model, and is calculated using CAPSO algorithm. Finally, the optimal allocation scheme is obtained after comprehensive considering the number and location of sensors. This article has20pairs of figure,11tables and71references.