摘要
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. 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 for comparison 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.
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammo- nia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the pro- duction efficiency. 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 for comparison 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.
基金
Supported by the National Natural Science Foundation of China (61074079)
Shanghai Leading Academic Discipline Project(B504)
Specialized Research Fund for the Doctoral Program of Higher Education of China (20100074120010)
the Natural Science Foundation of Shanghai City (11ZR1409700)
