Title :
Establishment and optimization of heating furnace billet temperature model
Author :
Fang, Xiaoke ; Yu, Liye ; Wang, Qi ; Wang, Jianhui
Author_Institution :
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
In the metallurgical industry, measuring the temperature distribution directly and accurately in billet heating process is a well-known difficult work. To improve the quality of heating billet, a billet temperature prediction model of heating furnace is necessary. Based on the characteristics of furnace section partition control, this paper firstly established a billet temperature prediction model with three serial neural networks as foundation, then optimized this model with the improved dynamically self-adaptive PSO. The simulation indicated that the establishment of this model is easy, the forecast precision and speed are obviously improved, and the match degree of prediction curve and actual curve is highly increased. All of these proved the effectiveness of this model.
Keywords :
billets; furnaces; heating; metallurgical industries; neurocontrollers; particle swarm optimisation; process control; temperature control; temperature measurement; actual curve match degree; billet heating process; billet temperature prediction model; furnace section partition control; heating billet quality improvement; heating furnace billet temperature model; metallurgical industry; neural networks; particle swarm optimization; prediction curve match degree; self-adaptive PSO; temperature distribution measurement; Billets; Convergence; Furnaces; Heating; Neural networks; Predictive models; Temperature; Billet Temperature Prediction Model; Dynamically Self-adaptive PSO; Heating Furnace; Neural Networks;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244379
