火电锅炉风机节能及氮氧化物减排研究
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摘要
火力发电厂是我国二次能源主要的生产者,同时也是主要的一次能源消耗者。火力发电厂在生产电能,消耗煤炭的同时,也排放了大量的氮氧化物等大气污染物;火电厂风机耗电占厂用电70%左右,节电潜力巨大。因此,提高火电厂生产效率,降低火电厂煤耗和污染成为我国实施节能减排政策的重要目标。
本文提出了一种具有相对较高控制品质的基于多模型理论的鲁棒预测PID控制方案。在广义预测控制(GPC)和理想PID控制基本算法的基础上设计了基于GPC的PID控制器;引入多模型控制思想,建立基于变工况增益调度的多模型预测PID控制策略;考虑闭环鲁棒稳定性,引入闭环系统鲁棒稳定准则,设计了多模型鲁棒预测PID控制器。该控制策略考虑了PID控制器广泛应用于工业现场的现状,继承了GPC算法处理工业过程对象控制的优势,对于火电厂一些重要的控制回路控制品质的提升有很好的实践和经济意义,仿真结果表明了该控制方案具有良好的控制效果。
本文对火电厂主要风机的类型、节电方式进行了较为详尽分析和总结;在基于燃烧优化的风机节能方面,对烟气含氧量控制回路的烟气含氧量最优设定值提出了新的计算方法,机理分析影响烟气含氧量的主要因素,以供电煤耗率最低为目标,采用一元线性回归的方法求取烟气含氧量最优设定值;在此基础上结合基于多模型理论的鲁棒预测PID控制策略给出了风机燃烧优化节电方案。
本文对火电厂氮氧化物的生成机理和主要的氮氧化物减排手段进行了较为详尽的阐述;通过机理分析影响氮氧化物排放的主要因素,建立了多元线性回归模型,并给出了较好的氮氧化物排放预测公式;以此多元线性回归模型为基础,给出了基于空气分级技术的氮氧化物减排方案。
Thermal power plants in China are both the major producer of electricity and the major consumer of coal resources. Coal-fired power plants pour a large volume of pollutants to the atmosphere, for example, nitrogen oxides while generating energy. It shows that the power consumption of fans in thermal power plant accounts for 70 percent of the power consumption of the whole thermal power plant. And some primary energy sources like coal are wasted in this process. Therefore, the energy-saving potential of the fans is big. Energy-saving and emission reduction in thermal power plants have come to be the most important objectives of energy policies in our country.
In this paper, a relatively high-quality robust predictive PID control scheme based on the theory of multi-model is proposed in detail. A PID controller based on generalized predictive control algorithm is designed at first. Second, multi-model predictive PID control strategy is established based on gain scheduling with introducing of multi-model thought. At last, considering the closed-loop robust stability of the control system, the robust predictive PID controller based on the theory of multi-model is designed successfully. The control strategy taking into account the advantages of both PID control and GPC control can improve the quality of some important control loops in thermal power plant, and the simulation results show that the control scheme has a good control performance.
In this paper, the types of the important fans in thermal power plant and the main energy-saving approaches are analyzed and summarized first. And a new method of calculating the set point of oxygen of flue gas is proposed based on the combustion optimization by analyzing the influencing factors, taking the lowest coal consumption as the goal, using the linear regression method, and the optimized set point of oxygen is calculated. At last, the energy-saving program is proposed combining with the robust predictive PID control scheme.
In this paper, the formation mechanism of nitrogen oxides in thermal power plant and the main measures of nitrogen oxides emission reduction are described in detail. And a forecast formula of nitrogen oxides is proposed with the method of multiple linear regression. Base on this mathematical model, the nitrogen oxides emission reduction programs is proposed at last.
本文提出了一种具有相对较高控制品质的基于多模型理论的鲁棒预测PID控制方案。在广义预测控制(GPC)和理想PID控制基本算法的基础上设计了基于GPC的PID控制器;引入多模型控制思想,建立基于变工况增益调度的多模型预测PID控制策略;考虑闭环鲁棒稳定性,引入闭环系统鲁棒稳定准则,设计了多模型鲁棒预测PID控制器。该控制策略考虑了PID控制器广泛应用于工业现场的现状,继承了GPC算法处理工业过程对象控制的优势,对于火电厂一些重要的控制回路控制品质的提升有很好的实践和经济意义,仿真结果表明了该控制方案具有良好的控制效果。
本文对火电厂主要风机的类型、节电方式进行了较为详尽分析和总结;在基于燃烧优化的风机节能方面,对烟气含氧量控制回路的烟气含氧量最优设定值提出了新的计算方法,机理分析影响烟气含氧量的主要因素,以供电煤耗率最低为目标,采用一元线性回归的方法求取烟气含氧量最优设定值;在此基础上结合基于多模型理论的鲁棒预测PID控制策略给出了风机燃烧优化节电方案。
本文对火电厂氮氧化物的生成机理和主要的氮氧化物减排手段进行了较为详尽的阐述;通过机理分析影响氮氧化物排放的主要因素,建立了多元线性回归模型,并给出了较好的氮氧化物排放预测公式;以此多元线性回归模型为基础,给出了基于空气分级技术的氮氧化物减排方案。
Thermal power plants in China are both the major producer of electricity and the major consumer of coal resources. Coal-fired power plants pour a large volume of pollutants to the atmosphere, for example, nitrogen oxides while generating energy. It shows that the power consumption of fans in thermal power plant accounts for 70 percent of the power consumption of the whole thermal power plant. And some primary energy sources like coal are wasted in this process. Therefore, the energy-saving potential of the fans is big. Energy-saving and emission reduction in thermal power plants have come to be the most important objectives of energy policies in our country.
In this paper, a relatively high-quality robust predictive PID control scheme based on the theory of multi-model is proposed in detail. A PID controller based on generalized predictive control algorithm is designed at first. Second, multi-model predictive PID control strategy is established based on gain scheduling with introducing of multi-model thought. At last, considering the closed-loop robust stability of the control system, the robust predictive PID controller based on the theory of multi-model is designed successfully. The control strategy taking into account the advantages of both PID control and GPC control can improve the quality of some important control loops in thermal power plant, and the simulation results show that the control scheme has a good control performance.
In this paper, the types of the important fans in thermal power plant and the main energy-saving approaches are analyzed and summarized first. And a new method of calculating the set point of oxygen of flue gas is proposed based on the combustion optimization by analyzing the influencing factors, taking the lowest coal consumption as the goal, using the linear regression method, and the optimized set point of oxygen is calculated. At last, the energy-saving program is proposed combining with the robust predictive PID control scheme.
In this paper, the formation mechanism of nitrogen oxides in thermal power plant and the main measures of nitrogen oxides emission reduction are described in detail. And a forecast formula of nitrogen oxides is proposed with the method of multiple linear regression. Base on this mathematical model, the nitrogen oxides emission reduction programs is proposed at last.
引文
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[5]李学颜,王勇.应用在线测量技术实现大型电厂煤粉锅炉节能减排[J].中国电力.2009,42(2):92-95.
[6]中国电力企业联合会科技服务中心.节能与控制[M].中国电力出版社,2008.
[7]李青山,高山,薛彦婷.火力发电厂节能技术及其应用[M].北京:中国电力出版社,2007.
[8]李利平.火电机组节能在线分析与智能运行优化方法研究[D].保定:华北电力大学,2007.
[9]姜胜文等.火电厂电气节能降耗的几种方法[J].内蒙古石油化工.2008,15:42-43.
[10]荆百林,李明,李敏.火电厂能耗评估方法的应用[J].电力设备.2008,9(3):31-33.
[11]王国弟,王健.电厂运行机组节能经济性评估的方法[J].上海节能.2006,4:18-21.
[12]白晓,张昌斌.火电厂节能减排设计的主要途径[J].节能环保.2005,10(5):71-77.
[13]张克凡,贾晖.浅谈发电厂节能降耗的有效措施[J].内蒙古石油化工.2008,5:95-96.
[14]丁立新主编.电厂锅炉原理[M].北京:中国电力出版社,2006.
[15]张健欣.流程工业预测控制技术概述[J].工业控制计算机.2007,20(6):1-3.
[16]许超,陈治纲,邵惠鹤.预测控制技术及应用发展综述[J].化工自动化及仪表.2002,29(3):1-10.
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[18]D.W.Culter,B.L.Ramaker.Dynamic matrix control-a computer control algorithm[J].AIChE National Meeting.1979.
[19]A.M.Morshedi.Universal dynamic matrix control[J].Third International Conference on Chemical Process Control.1986.
[20]P.M.Bruijn,L.J.Bootsma,H.B.Verbruggen.Predictive control using impulse response models[J].Digital Computer Applications to Process Control.1981.
[21]J.Richalet,et al.Predictive functional control:Application to fast and accurate robots[J].Automatic Control 10th Triennial World Congress of IFAC.1988.
[22]B.E.Ydstie.Extended horizon adaptive control[J].Proceedings of the 9th IFAC World Congress.1984.
[23]R.M.C.De Keyser,A.R.Van Cauwenberghe.Extended prediction self-adaptive control[J].Identification and system parameter estimation.1985.
[24]D.W.Clarke,C.Mohtadi,P.S.Tuffs.Generalized Predictive Control.Ⅰ.The Basic Algorithm[J].Automatica.1987,23:137-148.
[25]D.W.Clarke,C.Mohtadi,P.S.Tuffs.Generalized Predictive Control.Ⅱ.Extensions and Interpretations[J].Automatica.1987,23:149-160.
[26]C.E.Garcia,M.Morari.Internal model control:A unifying review and some new results[J].Industrial & Engineering Chemistry Process Design and Development.1982.
[27]席裕庚.预测控制[M].北京:国防工业出版社,1993.
[28]李嗣福等.计算机控制基础[M].合肥:中国科学技术大学出版社,2001.
[29]杨智,朱海锋,黄以华.PID控制器设计与参数整定方法综述[J].化工自动化及仪表.2005,32(5):1-7.
[30]胡国龙,孙优贤.多模型控制方法的研究进展及其应用现状[J].信息与控制.2004,33(1):72-75.
[31]郭启刚.热工过程多模型控制理论与方法的研究[D].保定:华北电力大学,2007.
[32]杨光军.电站锅炉燃烧状态监测与优化策略研究[D].保定:华北电力大学,2007.
[33]N.J.Rugh.Analytical framework for gain scheduling[J].IEEE Control.Syst Magazine.1991,11:79-84.
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