摘要
脱硝反应器入口NO_X浓度的及时、准确测量,对精确调节喷氨量、控制氮氧化物的排放至关重要。针对NO_X气体分析仪测量存在的精度差、滞后性等问题,基于传统云理论,并结合径向基函数(RBF)神经网络,提出了改进的云自适应粒子算法(CPSO)-RBF神经网络的测量模型。利用云模型理论中云滴具有随机性、稳定倾向性等特点,提出了一种新型分段式自适应调整粒子群惯性权重算法。利用此优化算法,对神经网络参数进行优化,提高了测量模型的精度。将该模型应用于SCR反应器入口的NO_X含量测量中,实例仿真表明,改进算法优化的神经网络模型具有较高的精度,为反应器入口NO_X含量的实时、准确测量提供了一定的理论依据,也为实际生产过程中NO_X的测量与控制提供了一定的参考。
The timely and accurate measurement of NO_X content at inlet of denitrification reactor is very important to accurately adjust the amount of ammonia spray and the NO_X emission control. Aiming at the problems of the serious delay and poor precision of the NO_X gas analyzer,based on traditional cloud theory and combining with the radial basis function（ RBF） neural network,the measurement model based on CPSO-RBF neural network is proposed. By using the features of cloud droplets,i. e.,randomness and stable tendency,the new type of segmented adaptive adjustment particle swarm inertia weight algorithm is proposed. The parameters of neural network are optimized using this optimization algorithm,thus the accuracy of the measurement model is enhanced. The model is applied in the NO_X measurement at the inlet of SCR reactor,the simulation of practical example indicates that the neural network model optimized by the improved algorithm features high accuracy,it provides certain theoretical basis for real time and precise measurement of NO_X at inlet of the reactor; and certain reference for NO_X measurement and control in practical production process.
出处
《自动化仪表》
CAS
2017年第7期75-79,共5页
Process Automation Instrumentation
关键词
脱硝反应器
气体分析仪
云模型
粒子群优化算法
自适应调整
神经网络
SCR
软测量
惯性权重
Denitrification reactor
Gas analyzer
Cloud model
Particle swarm optimization algorithm
Adaptive adjustment
Neural network
SCR
Soft measurement
Inertia weight
