Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved particle swarm optimization and BP neural network

• Published in: Chemical engineering research & design Vol. 89; no. 10; pp. 2102 - 2109
• Main Authors: Xu, Wei, Zhang, Lingbo, Gu, Xingsheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2011; Elsevier
• Subjects: Ammonia; Ammonia concentration; Ammonia synthesis; Applied sciences; Artificial neural networks; Back propagation; Chemical engineering; Exact sciences and technology; Mathematical models; Optimization; Outlets; Particle swarm optimization; Sensors; Soft sensor; Synthesis; Soft sensor; Ammonia synthesis; Ammonia concentration; Particle swarm optimization; Chemical industry; Neural network; Modeling; Optimization; Software sensor; Fertilizer plant; Production; Reliability; Expansion
• ISSN: 0263-8762
• DOI: 10.1016/j.cherd.2010.12.015

▶ Two operations are introduced into the basic PSO algorithm, and an improved algorithm called PSOEC is proposed. ▶ BP neural network is used to establish a soft sensor for estimating the ammonia concentration at the ammonia converter outlet in a real-world plant. ▶ The proposed PSOEC algorithm is employed as the learning method of the weights and thresholds of BP neural network. Ammonia synthesis production is a critical chemical industry around the world. As the key process variable, the ammonia concentration at the ammonia converter outlet reflects the production status and provides good advices for the operators. However, it cannot be easily measured because of high expenditure and deficient reliability of online sensors in a real-world ammonia synthesis process. Due to this, a soft sensor, which is used to predict the outlet ammonia concentration, is developed using BP neural network (BPNN). An improved particle swarm optimization with expansion and constriction operation (PSOEC) is proposed to optimize the weights and thresholds of BPNN. The PSOEC and BPNN based soft-sensing model (PSOEC-NN) is applied to inferring the outlet ammonia concentration in a fertilizer plant. Results using other modeling methods (BPNN and PSO-NN) are presented for comparison purpose. The proposed PSOEC-NN based soft sensor shows high precision and good generalization capability. PSOEC-NN model would offer great help for further work like advanced control and operational optimization in the ammonia synthesis process.