炼焦烟气脱硝过程建模与运行优化控制方法研究

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	炼焦烟气脱硝过程建模与运行优化控制方法研究
	李亚宁1,2
	2018-05
学位类型	工学博士
中文摘要	随着国家大气环境治理的不断深入，新的炼焦工业污染物排放标准提出更加严格的焦炉二氧化硫和氮氧化物排放指标。对于国内近千家焦化企业来说，实施炼焦烟气治理，满足新环保标准要求已经“迫在眉睫”。鉴于此，国内某焦化集团投入并运行首套烟气脱硫脱硝一体化装置，但现阶段以人工操作为主的运行方式不仅无法保证烟气净化效果，同时存在较大的能源浪费。本课题以国内首座炼焦烟气湿式脱硫脱硝装置为研究对象，结合炼焦生产的特殊性和复杂性，进行脱硝过程建模与运行优化控制方法的研究，旨在形成高效、可靠、经济的炼焦烟气脱硝过程成套控制技术，推动湿式脱硫脱硝技术的产业化推广，促进我国焦化工业烟气的综合治理，具有重大的经济效益和社会效益。本文的主要工作和贡献如下：（1）首先进行炼焦烟气脱硫脱硝一体化过程分析。过程模型的建立及装置的运行优化与控制建立在对过程特性充分了解的基础之上，目前尚没有对焦炉烟气湿式脱硫脱硝工艺机理方面的研究。结合相近工艺的研究成果及部分现场实验确定影响烟气脱硝效果的主要因素，分析该过程控制难点、运行现状以及存在的问题，最后提出炼焦烟气脱硝过程优化控制的总体结构，指出了后续章节的主要研究内容。（2）针对炼焦生产工况变化极其频繁，而当前烟气分析仪位于装置入口处，焦炉烟道与传感器之间传输滞后极大，无法及时准确地依靠人工经验判断烟气指标状态的问题，本文以焦炉加热燃烧过程为背景，提出了基于烟气指标软测量模型的状态估计方法。首先根据物料守恒及反应动力学建立烟气指标的机理模型，与基于改进神经网络的补偿模型并联组成集成烟气指标软测量模型；在集成模型的基础上，提出了利用时间窗的指标稳态值计算方法及燃烧工况变动的判断准则，为后文焦炉换向过程脱硝控制及脱硝过程的优化运行工作奠定了基础。（3）针对焦炉周期性换向期间烟气脱硝过程能源浪费严重的问题，本文分析了换向过程烟气指标变化的原因，提出了换向过程烟气NO_x浓度扰动建模与臭氧量控制方法：将烟气NO_x扰动模型分为入口扰动模型与出口扰动模型，利用连续脉冲信号与一阶惯性传递函数串联的响应曲线作为通用模型结构，分别对入口和出口NO_x扰动进行建模与参数辨识。设计前馈控制系统，并给出不同入口烟气换向过程最低NO_x浓度和出口NO_x浓度限定值关系下的前馈控制策略，能够最大限度地节省臭氧发生机组电耗成本。（4）针对复杂运行条件下炼焦烟气脱硝过程的实时操作优化问题，本文提出一种数据驱动的脱硝过程操作参数实时优化方案，整个优化过程分为两步：首先基于案例推理（Case Based Reasoning, CBR）方法及反馈调节器得到优化指标的预设定值，考虑到传统案例重用方法的缺点，提出了基于主成分回归（Principal Component Regression, PCR）-多案例融合的案例检索与重用方法；然后提出一种基于时间有序性即时学习（Time-order Just-in-time Learning, T-JITL）算法的局部非参数建模方法，将此模型作为优化约束条件，利用优化算法进一步求解优化问题得到最优值。该方法不基于任何参数模型，同时充分考虑了炼焦生产工况的复杂性、时序性，解决了炼焦烟气脱硝复杂工况所导致的稳态模型难以获取的问题，完全基于数据得到优化设定值。（5）结合本文研究成果，基于现场已有的软硬件平台，设计并实现炼焦烟气脱硝过程优化控制原型系统，工业现场的应用验证结果表明，本文提出的技术路线和方法具有较强的实用性，能够实现脱硝过程的稳定、经济运行。
英文摘要	With the deepening of atmospheric environmental governance in China, the new pollutants emission standard of coking industry puts forward stricter restrictions on coke oven emissions of SO₂ and NO_x. As a result, it is imminent for nearly 1000 domestic coking enterprises to implement coking flue gas treatmemnt so as to meet the national standard. In view of this increased pressure, a domestic coking group has taken the lead in building and operating an coking flue gas desulfurization and denitration integrated device, however, not only the flue gas purification effect can not be satisfied, enormous waste of energy also exists due to the manual operation mode at the present stage. In this dissertation, we take the coking flue gas desulfurization and denitration integrated device as the research object, the modeling, operation optimization and control strategy of denitration process is studied in consideration of the particularity and complexity of coking production, aiming to form a efficient, reliable and economical matching control technology for coking flue gas denitration process, promoting the industrialization of wet desulfurization and denitration technology as well as the flue gas comprehensive governance of coking industry in China. More specifically, the main content of this paper has been concluded as bellow: (1) The coking flue gas desulfurization and denitration process is analysed. The modeling and operation optimization of the process are based on fully understanding of the process characteristics, however, there is no mechanism research of the coking flue gas wet desulfurization and denitration process. The main influencing factors of the denitration process are determined first using the existing research results of similar processes and some experiments. Then the difficulties, operation status and existing problems of the process are analysed. Finally, the overall structure of the optimization control of coking flue gas denitrification process is proposed, pointing out the main research contents of the following chapters. (2) The current condition of inlet flue gas indices cannot be determined timely and precisely by experience owing to the large detection lag and complex upstream coking process. In order to solve this problem, we take the heating combustion process of the coke oven in the coking plant as the background, an state estimation method based on the flue gas soft sensor model is proposed. The mechanism models are established firstly according to the principle of material balance and reaction kinetics, an improved neural network combining optimal stopping principle and dual momentum adaptive learning rate is proposed and used to compensate the prediction error. Based on the integrated model, we propose a steady-state value calculating method of the coking flue gas indices by using time windows and the combustion condition change criterion, laying the foundation for the coke oven reversing process control and operation optimization of the denitration process in the later chapters. (3) A large amount of energy is wasted in the coking flue gas denitration process during the periodic coke oven reversing operation. In order to solve this problem, causes to NO_x concentration change during this process are thoroughly analyzed, the disturbance modeling method of flue gas NO_x concentration and control strategy of the ozone output are proposed: The flue gas NO_x disturbance models are divided into the inlet and outlet disturbance models, the response curve of the pulse signal and a first-order inertial transfer function, which are connected in series is taken as the general model structure, the NO_x disturbance models can be established after parameters being identified. Then the feedforward control system is designed, and control strategy according to the relation between the lowest inlet flue gas NO_x concentration in reversing process and the limited value of outlet NO_x concentration is proposed, which can minimize the ozone generating unit power consumption cost. (4) For the real-time operation optimization problem under complex operating condition of coking flue gas denitration process, we propose a data-based operating parameter real-time optimization strategy of the denitration process, which is a two-step process. First, the preset operational indices are obtained by case based reasoning (CBR) method and a feedback regulator, a case reuse method based on principal component regression (PCR) multicase fusion is proposed to solve the shortcomings of the traditional case reuse method. Then a local nonparametric modeling method based on time-order just-in-time learning (T-JITL) algorithm is proposed, the model will be used as the optimization constraints, and the optimal value can be obtained by solving the established optimization problem. This scheme is not based on any parameter model, moreover with the complexity and time-order of coking production being taken into account, which can solve the steady-state modeling difficulties caused by the complex coking flue gas denitration conditions and obtain the optimized set points based on data. (5) Using the methods proposed before, a prototype system for coking flue gas denitration process optimization based on the existing hardware and software platform is designed and developed. The application verification results in industrial field show that the method proposed in this paper has strong practicability and can realize stable and economic operation of the denitration process.
关键词	炼焦烟气脱硫脱硝换向过程运行优化数据驱动
文献类型	学位论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21177
专题	毕业生_博士学位论文
作者单位	1.中国科学院自动化研究所 2.中国科学院大学
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	李亚宁. 炼焦烟气脱硝过程建模与运行优化控制方法研究[D]. 北京. 中国科学院研究生院,2018.

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