基于多元统计方法的连续重整装置的性能监控和优化
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摘要
连续重整装置是重要的石油二次加工装置,它的运行过程是一个复杂的工业过程,具有不确定性(环境结构和参数的未知性、时变性、随机性、突变性)、非线性、变量间的关联性以及信息的不完全性和大纯滞后性等,这种变量之间相关关系的稳定性和生产过程平稳性以及产品质量的一致性有着密切的联系,用传统的方法分别对多个变量进行单变量监控和性能优化无法得到较好的效果,且存在着较大地困难。
我国现有的连续重整装置均是采用了美国的UOP技术或法国的IFP技术,由于受核心技术保密的影响,没有正式公开的、适用面非常广泛的反应器机理模型可以参考,连续重整装置的控制性能的提高和完善还受制于人。根据连续重整装置的过程监测变量较多、其DCS系统能提供大量的实时数据的特点,本文以多变量统计方法为理论基础,建立了基于数据驱动的数学模型对连续重整装置的生产运行过程进行计算机监控和优化控制。本文的研究工作主要包括如下几个部分:
1)根据多变量统计理论在分析了连续重整装置工艺过程和重整加热炉传热原理后,提出了需监控的参数,首次运用基本主元分析(PCA)建立了基于数据驱动的监控数学模型,根据平方预测误差(SPE)、T2图、主元得分图、贡献图等监测和诊断连续重整装置运行中的故障。针对各变量间可能存在的非线性及所采集数据中存在着多尺度的噪声污染,改进了基本主元分析法,多尺度线性主元分析、多尺度非主元分析的应用结果表明:非线性方法有效地对数据进行了压缩,小波变换在各个尺度上滤除了噪声污染,且克服了PCA不能监测出数据中小的偏差、并会延迟检测出大的偏差的缺点。
2)针对观测数据并非均服从正态分布的事实和主元分析只能利用二阶统计信息的不足,首次提出了一种基于独立变量分析(ICA)的连续重整装置的监控方法,采用非高斯最大化判据从观测变量中分解出了相互独立的非高斯量,不仅满足了PCA所要求的不相关,更满足统计意义下的独立特性。在选定了独立变量和建立了相应的统计控制限后,对连续重整装置的运行进行了监控,结果表明比主元分析法具有更少的故障误报率和漏报率。
3)讨论了影响积灰结垢的主要因素和分析、比较了国内外现有的积灰量测量方法,提出并推导了基于多变量统计过程理论的积灰量在线测量模型,为重整加热炉的优化控制提供了衡量指标。
4)首次建立了基于主元控制器的优化控制系统,通过和原DCS系统的有效通讯,完善了原DCS系统的控制性能,在氧含量控制模块中,通过热效率反馈氧含量动态寻优和预测控制,根据氧含量的软测量值实时调节重整加热炉的烟道挡板的开度,实现加热炉的在线燃烧优化,提高了传热的效率。
CCR is a secondary oil processing unit. It is claimed as an indispensable fundamental production element in a modern refinery and/or petrochemical facilities. As a rather complicated process, it is characterized by uncertainties (i.e. unidentified environmental structure and parameters, time-varying, randomness, mutation), non-linearity, correlations among variates and incompletion or lag of information. Stability of variate correlations directly impacts constant operation process and product quality consistency, hence it is impossible to achieve satisfactory univariate monitoring, not to mention optimization, of various variates. Furthermore, when there is big noise around in operation, it would become even harder to pre-alarm any possible failure(s) through univariate monitoring.
In All CCR units in China today are stereotyped with technologies of either UOP, USA or IFP, France. Due to patent /technology confidentiality and insufficient app- rehension of characteristics of specific elements like reactor, regenerator, etc. plus the unavailability of publicly disclosed multi-purpose reactor mechanism model for reference, so far upgrading and improvement of CCR’s controllability are still relying on out-sources. Taking into consideration that a CCR is of relatively more variates for process monitoring and its DCS is able to generate a large number of real data, this article builds up a data-driven mathematical model based on Multivariate Statistics and by means of data collection to realize the computer monitoring and optimization control of CCR operation. The content of this article covers mainly the following sections:
1) Through analyzing the CCR process and heat transferring principle of CCR furnace based on the Multivariate Statistics Theory, we identified all parameters to be monitored and build up accordingly a data-driven monitoring mathematical model by means of Principle Component Analysis (PCA) for the first time, which can monitor and diagnose the failures in CCR operation with applications of Squared Prediction Error(SPE), Hotelling T2, Principle Component Scores charts, and variates contribution charts. The PCA is improved against the possible non-linearity among variates and the multiscale noise pollutions existing in collected datum. The application result of multiscale linear PCA and multiscale NPCA shows that non-linearity analysis constricts the data effectively, wavelet transform filters the noise pollution in all scales, and avoid the defects of impossible detection of minor deviation and delayed identification of major deviation in datum by PCA.
2) Due to the fact that not all observed data obey normal distribution and the shortcoming that PCA can only make use of second order statistic, this article puts forward a kind of Independent Component Analysis (ICA) based monitoring measure for CCR unit. The independent non-gauss value is developed from observed variates by means of non-gauss maximization criteria, which meets not only the non-correlation required by PCA, but also the independence characteristic in the sense of statistics. After we selected independent variates and established relative statistic control limitation, we monitored the CCR operation and the result revealed that there was less faulty alarms and missed alarms than that produced by PCA.
3) After the discussion of primary factors causing the dust aggradation and scale formation, and analysis and comparison of domestic and overseas dust amount measurement methods, we present and deduce an on line dust measurement model based on multivariate statistics, which provides a criteria for evaluating the optimized control of CCR furnace.
4) An optimization control system based on PCA Controller is built up for the first time, and through effective communications with the original DCS, controlling performance of the original DCS is improved and heat transform of CCR furnace optimized. In the control loop of control oxygen content ,through feedback calculation of heat efficiency to obtain optimal oxygen content in fuel gas and to control oxygen content to reach optimal set point by self-adjust ladderly generalized predictive、control can raise heat efficiency of heating furnace.
我国现有的连续重整装置均是采用了美国的UOP技术或法国的IFP技术,由于受核心技术保密的影响,没有正式公开的、适用面非常广泛的反应器机理模型可以参考,连续重整装置的控制性能的提高和完善还受制于人。根据连续重整装置的过程监测变量较多、其DCS系统能提供大量的实时数据的特点,本文以多变量统计方法为理论基础,建立了基于数据驱动的数学模型对连续重整装置的生产运行过程进行计算机监控和优化控制。本文的研究工作主要包括如下几个部分:
1)根据多变量统计理论在分析了连续重整装置工艺过程和重整加热炉传热原理后,提出了需监控的参数,首次运用基本主元分析(PCA)建立了基于数据驱动的监控数学模型,根据平方预测误差(SPE)、T2图、主元得分图、贡献图等监测和诊断连续重整装置运行中的故障。针对各变量间可能存在的非线性及所采集数据中存在着多尺度的噪声污染,改进了基本主元分析法,多尺度线性主元分析、多尺度非主元分析的应用结果表明:非线性方法有效地对数据进行了压缩,小波变换在各个尺度上滤除了噪声污染,且克服了PCA不能监测出数据中小的偏差、并会延迟检测出大的偏差的缺点。
2)针对观测数据并非均服从正态分布的事实和主元分析只能利用二阶统计信息的不足,首次提出了一种基于独立变量分析(ICA)的连续重整装置的监控方法,采用非高斯最大化判据从观测变量中分解出了相互独立的非高斯量,不仅满足了PCA所要求的不相关,更满足统计意义下的独立特性。在选定了独立变量和建立了相应的统计控制限后,对连续重整装置的运行进行了监控,结果表明比主元分析法具有更少的故障误报率和漏报率。
3)讨论了影响积灰结垢的主要因素和分析、比较了国内外现有的积灰量测量方法,提出并推导了基于多变量统计过程理论的积灰量在线测量模型,为重整加热炉的优化控制提供了衡量指标。
4)首次建立了基于主元控制器的优化控制系统,通过和原DCS系统的有效通讯,完善了原DCS系统的控制性能,在氧含量控制模块中,通过热效率反馈氧含量动态寻优和预测控制,根据氧含量的软测量值实时调节重整加热炉的烟道挡板的开度,实现加热炉的在线燃烧优化,提高了传热的效率。
CCR is a secondary oil processing unit. It is claimed as an indispensable fundamental production element in a modern refinery and/or petrochemical facilities. As a rather complicated process, it is characterized by uncertainties (i.e. unidentified environmental structure and parameters, time-varying, randomness, mutation), non-linearity, correlations among variates and incompletion or lag of information. Stability of variate correlations directly impacts constant operation process and product quality consistency, hence it is impossible to achieve satisfactory univariate monitoring, not to mention optimization, of various variates. Furthermore, when there is big noise around in operation, it would become even harder to pre-alarm any possible failure(s) through univariate monitoring.
In All CCR units in China today are stereotyped with technologies of either UOP, USA or IFP, France. Due to patent /technology confidentiality and insufficient app- rehension of characteristics of specific elements like reactor, regenerator, etc. plus the unavailability of publicly disclosed multi-purpose reactor mechanism model for reference, so far upgrading and improvement of CCR’s controllability are still relying on out-sources. Taking into consideration that a CCR is of relatively more variates for process monitoring and its DCS is able to generate a large number of real data, this article builds up a data-driven mathematical model based on Multivariate Statistics and by means of data collection to realize the computer monitoring and optimization control of CCR operation. The content of this article covers mainly the following sections:
1) Through analyzing the CCR process and heat transferring principle of CCR furnace based on the Multivariate Statistics Theory, we identified all parameters to be monitored and build up accordingly a data-driven monitoring mathematical model by means of Principle Component Analysis (PCA) for the first time, which can monitor and diagnose the failures in CCR operation with applications of Squared Prediction Error(SPE), Hotelling T2, Principle Component Scores charts, and variates contribution charts. The PCA is improved against the possible non-linearity among variates and the multiscale noise pollutions existing in collected datum. The application result of multiscale linear PCA and multiscale NPCA shows that non-linearity analysis constricts the data effectively, wavelet transform filters the noise pollution in all scales, and avoid the defects of impossible detection of minor deviation and delayed identification of major deviation in datum by PCA.
2) Due to the fact that not all observed data obey normal distribution and the shortcoming that PCA can only make use of second order statistic, this article puts forward a kind of Independent Component Analysis (ICA) based monitoring measure for CCR unit. The independent non-gauss value is developed from observed variates by means of non-gauss maximization criteria, which meets not only the non-correlation required by PCA, but also the independence characteristic in the sense of statistics. After we selected independent variates and established relative statistic control limitation, we monitored the CCR operation and the result revealed that there was less faulty alarms and missed alarms than that produced by PCA.
3) After the discussion of primary factors causing the dust aggradation and scale formation, and analysis and comparison of domestic and overseas dust amount measurement methods, we present and deduce an on line dust measurement model based on multivariate statistics, which provides a criteria for evaluating the optimized control of CCR furnace.
4) An optimization control system based on PCA Controller is built up for the first time, and through effective communications with the original DCS, controlling performance of the original DCS is improved and heat transform of CCR furnace optimized. In the control loop of control oxygen content ,through feedback calculation of heat efficiency to obtain optimal oxygen content in fuel gas and to control oxygen content to reach optimal set point by self-adjust ladderly generalized predictive、control can raise heat efficiency of heating furnace.
引文
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