文献类型：期刊文献

中文题名：基于改进的GSO算法和BP神经网络的氨合成塔出口氨含量软测量模型(英文)

英文题名：Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network

作者：阎兴頔[1];杨文[1];马贺贺[1];侍洪波[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2012

卷号：20

期号：6

起止页码：1184

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;EI(收录号：20130215898820);Scopus;CSCD:【CSCD2011_2012】；

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)

语种：中文

中文关键词：BP神经网络;软测量模型;氨浓度;氨合成塔;优化;出口;搜索;应用程序

外文关键词：ammonia synthesis, ammonia concentration, soft sensor, group search optimization

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.