Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved group search optimization and BP neural network
The ammonia synthesis section is the core during the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects the production efficiency directly. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied to compare with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
Ammonia synthesis Ammonia concentration Soft sensor Group search optimization.
Xingdi Yan Wen Yang Hehe Ma Hongbo Shi
Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,
国内会议
厦门
英文
1-7
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）