SCR烟气脱硝系统建模及优化控制
发布时间：2018-11-17 16:25
【摘要】：随着当前以酸雨、雾霾为首的大气污染问题日益严重,环境污染的治理逐渐成为能源生产的重要环节。SCR脱硝技术以其成熟、应用广泛的特点成为我国燃煤电厂控制NOX污染的主要手段之一。深入研究SCR脱硝技术,针对SCR脱硝系统变工况运行的特性,选用试验建模的方法,选取能够反应系统动态特性的输入输出数据,建立有效的SCR脱硝系统模型,以此作为优化控制过程的基础,是提高脱硝效率的重要途径,对SCR脱硝装置的设计和运行具有重要的参考价值。本文以火电厂变负荷运行常态为背景,综述了SCR脱硝技术及应用,基于对模型泛化能力强,推广性可靠,计算速度快,适于在线工作的考虑,选用了最小二乘支持向量机的方法建立非线性模型。针对变量之间的多重共线性,首先采用了相似度函数优化处理方法,去除变量冗余数据;其次为了更好的体现系统对象的动态特性,利用核主元分析和多变量过程监测影响SCR反应器入口NOX浓度的主导因素,采用网格搜索结合粒子群(PSO)算法确定模型中参数,建立了LSSVM预测模型。针对机组变负荷要求,本文结合滑动窗口法,预定义可根据锅炉负荷的变化而自适应改变的更新阈值,分别是基于剔除最旧数据准则和基于预报误差来自适应更新模型参数,建立了两种动态模型。分别将上述建模方法应用于SCR脱硝系统建模,进行了测试,对比结果显示出了动态模型的预测精度较高、泛化能力强。最后将预测模型应用到预测控制算法中,转化为非线性优化问题,利用粒子群算法优化喷氨量,使SCR反应器出口NOX浓度能够很好地跟踪设定值,从而提高脱硝效率。实验结果表明相比传统的PID控制方式,该方法实现了较好的控制效果。
[Abstract]:With the acid rain, the air pollution problem led by haze is becoming more and more serious, and the control of environmental pollution has gradually become an important link in the energy production. SCR denitrification technology is mature with its maturity. The widely used features have become one of the main means to control NOX pollution in coal-fired power plants in China. The technology of SCR denitrification is studied in depth. According to the characteristics of SCR denitrification system under different operating conditions, the experimental modeling method is selected, and the input and output data which can reflect the dynamic characteristics of the system are selected to establish an effective SCR denitrification system model. As the basis of optimization control process, it is an important way to improve denitrification efficiency and has important reference value for the design and operation of SCR denitrification plant. Based on the background of variable load operation normality of thermal power plant, this paper summarizes the technology and application of SCR denitrification, which is based on the consideration of strong generalization ability, reliable generalization, fast calculation speed and suitable for on-line operation. The least square support vector machine (LS-SVM) is used to establish the nonlinear model. Aiming at the multiple collinearity between variables, the similarity function optimization method is used to remove the redundant data. Secondly, in order to better reflect the dynamic characteristics of the system object, the kernel principal component analysis and multivariable process are used to monitor the main factors affecting the NOX concentration at the inlet of SCR reactor, and the parameters in the model are determined by grid search combined with particle swarm optimization (PSO) algorithm. LSSVM prediction model is established. According to the variable load requirement of the unit, this paper combines the sliding window method to predefine the renewal threshold which can be changed adaptively according to the change of boiler load, which is based on the elimination of the oldest data criterion and the prediction error based on the adaptive updating model parameters, respectively. Two dynamic models are established. The above modeling methods are applied to the modeling of SCR denitrification system respectively. The comparison results show that the dynamic model has high prediction accuracy and strong generalization ability. Finally, the predictive model is applied to the predictive control algorithm, which is transformed into a nonlinear optimization problem. The particle swarm optimization algorithm is used to optimize the amount of ammonia injection, so that the NOX concentration at the outlet of the SCR reactor can track the set value well, thus improving the denitrification efficiency. The experimental results show that this method has better control effect than the traditional PID control method.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X773
[Abstract]:With the acid rain, the air pollution problem led by haze is becoming more and more serious, and the control of environmental pollution has gradually become an important link in the energy production. SCR denitrification technology is mature with its maturity. The widely used features have become one of the main means to control NOX pollution in coal-fired power plants in China. The technology of SCR denitrification is studied in depth. According to the characteristics of SCR denitrification system under different operating conditions, the experimental modeling method is selected, and the input and output data which can reflect the dynamic characteristics of the system are selected to establish an effective SCR denitrification system model. As the basis of optimization control process, it is an important way to improve denitrification efficiency and has important reference value for the design and operation of SCR denitrification plant. Based on the background of variable load operation normality of thermal power plant, this paper summarizes the technology and application of SCR denitrification, which is based on the consideration of strong generalization ability, reliable generalization, fast calculation speed and suitable for on-line operation. The least square support vector machine (LS-SVM) is used to establish the nonlinear model. Aiming at the multiple collinearity between variables, the similarity function optimization method is used to remove the redundant data. Secondly, in order to better reflect the dynamic characteristics of the system object, the kernel principal component analysis and multivariable process are used to monitor the main factors affecting the NOX concentration at the inlet of SCR reactor, and the parameters in the model are determined by grid search combined with particle swarm optimization (PSO) algorithm. LSSVM prediction model is established. According to the variable load requirement of the unit, this paper combines the sliding window method to predefine the renewal threshold which can be changed adaptively according to the change of boiler load, which is based on the elimination of the oldest data criterion and the prediction error based on the adaptive updating model parameters, respectively. Two dynamic models are established. The above modeling methods are applied to the modeling of SCR denitrification system respectively. The comparison results show that the dynamic model has high prediction accuracy and strong generalization ability. Finally, the predictive model is applied to the predictive control algorithm, which is transformed into a nonlinear optimization problem. The particle swarm optimization algorithm is used to optimize the amount of ammonia injection, so that the NOX concentration at the outlet of the SCR reactor can track the set value well, thus improving the denitrification efficiency. The experimental results show that this method has better control effect than the traditional PID control method.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X773
【参考文献】
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