基于半监督回归的多模型在线软测量系统研究
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摘要
随着现代社会经济与科技的迅速发展,由于工艺、环保等的要求,工业生产过程变得越来越复杂,许多工业过程都出现了多工况、变化大、非线性强的特点。为了能够更加准确地对系统进行控制,工业过程中的生产变量应该能够实时地、准确地反映当前生产的状态,所以这些生产变量的测量成为了工业控制系统的重要组成部分。如果采用传统的传感器采集方法,往往投入和维护成本较高,而且有些变量使用传感器测量存在一定的延迟,并不能有效地反映实时变化情况,软测量技术目前是解决这类变量测量问题的有效方法之一。对于那些多工况的非线性复杂工业过程,系统的工矿处于不断变化中,单一的静态模型有时不能反映复杂工业过程的实际状况,如果在软测量技术中辅以在线技术和多模型技术,可以有效地提高测量精度和可靠性。
本文主要针对工矿不断变化的复杂工业过程,进行了基于半监督回归的在线和多模型软测量方法的研究,并运用仿真和实例证明了所研究方法的正确性和实用性。主要研究工作如下:
1.阐述了软测量技术的概念、步骤和建模方法;介绍了半监督技术、在线技术以及多模型技术的理论基础和发展情况。
2.提出了一种基于局部线性回归的半监督在线软测量方法。该方法以局部线性回归为基础,通过对目标函数引入未标记数据的方法,实现算法的半监督化,仿真证明了半监督算法可以有效地提高预测精度。针对算法中参数选择的问题,提出了一种半监督局部线性回归的自适应参数选择方法,该方法利用软测量主导变量的斜率估计值对算法中的高斯核宽进行自适应选择。最后,在此基础上,本文提出了一种基于滚动时间窗的半监督在线软测量方法,该方法能够使软测量模型实时更新,适应输入数据的变化,仿真结果证明了采用自适应参数的半监督在线算法拟合性更好。
3.本文对多模型软测量系统进行了研究。针对多工况、非线性强的工业过程,提出了两种基于半监督回归的多模型软测量系统建模方法:加权式多模型建模方法和切换式多模型建模方法。加权式多模型建模方法首先根据输入数据的特点建立多个类别的子模型进行预测,然后对输入数据进行模糊聚类分析,得到每个输入数据点对所有子模型的模糊隶属度,最后利用该模糊隶属度对所有子模型的预测结果进行加权计算,得到多模型的预测结果;切换式多模型建模方法首先对所有的输入数据进行分类,接着根据数据点类别的不同采用不同的子模型进行预测,并将预测结果直接作为多模型的预测结果。本文对这两种多模型方法均进行了仿真检验,结果表明多模型系统确实可以有效提高预测精度。
4.最后,设计了一套基于半监督回归的多模型软测量系统应用方案,该方案以超超临界机组为研究对象,烟气含氧量为主导变量,以及其他从工业生产中获得的十几组变量数值作为辅助变量。方案一共分为两个部分:离线部分和在线部分,本文对其中的步骤做了详细说明,比如工况分析、数据预处理等等。最后利用该方法对超超临界机组烟气含氧量进行了多模型软测量预测,预测结果证明了方法的正确性和实用性。
With development of modern society and demand of process technologies and environmental protection, industrial process has became more and more complex and is characterized by multi-operating conditions, time-variation and strong nonlinearity. In order to control processes accurately, process variables should be able to reflect the state of processes real-timely and accurately, therefore, the measurement of these variables is becoming an important part of industrial control system. If use the traditional method of sensor acquisition to measure these variables, the investment and maintenance costs are usually very high, and even there are delays in some variables’measurements, so sensor acquisition sometimes cannot reflect the changes of processes in real time. Nowadays, soft sensor becomes an effective way to solve the problem of these variables’measurements. For those non-linear and multi-state industrial processes whose operating conditions often change, a single static model usually cannot reflect the actual situation of the process correctly, but if combine soft sensor technology with online modeling technology and multi-modeling technology, the accuracy and reliability of soft sensor measurement can be improved effectively.
In this paper, a research of online and multi-model soft sensor system basing on semi-supervised regression is done for those complex industrial processes whose operating conditions often change. Simulations and application have shown correctness and practicality of this research. All of the work is listed as below:
1. Elaborated on concepts of soft sensor technology’s procedures and modeling methods; introduced semi-supervised technology, online technology and multi-model technology and their developments.
2. Proposed an online soft sensor modeling method basing on semi-supervised Local Linear Regression. This method makes Local Linear Regression become a semi-supervised method by importing unlabeled data to its objective function, and simulations proved the semi-supervised method can improve the system’s accuracy successfully. For parameters’selecting, this paper proposed an adaptive method which selects Gaussian kernel width by dominate variable’s estimated slope in semi-supervised Local Linear Regression. At last, basing on the above research, this paper used rolling time window to propose an online method of SSLLR, and this method makes soft sensor model can be updated in real time and be able to adaptive to input data, simulation result shows that the adaptive parameter selection method makes SSLLR have a better prediction.
3. Researched on multi-model soft sensor modeling. For those industrial processes which have multi-operating conditions and strong nonlinearity, this paper proposed two multi-model soft sensor modeling methods basing on semi-supervised regression: weighted multi-modeling method and switched multi-modeling method. Weighted multi-modeling method uses all sub-models which are created by input data’s characteristic to predict the dominate variable at first, then gets fuzzy membership degrees of all input data by fuzzy clustering method, and uses these membership degrees as weights to compute multi-model prediction result by all sub-models’predictions at last; Switched multi-modeling method clusters all input data into different categories at first, and uses only one specific sub-model to predict the dominate variable, and this sub-model is determined by input data’s characteristic, finally makes the sub-model prediction result be the multi-model prediction result directly. Both of the two multi-modeling methods are simulated in this paper, and the results show that multi-model can really improve the accuracy of prediction.
4. At last, this paper designed a multi-model soft sensor system’s application based on semi-supervised regression. The application focus on ultra-supercritical units, and flue gas oxygen content is chosen as the dominate variable, besides, a dozen sets of variables which are obtained from industrial production are chosen as the auxiliary variable. The application is designed into two parts: offline part and online part. Detailed descriptions of these steps, such as working conditions analysis, data preprocessing, are illustrated in this paper. At last, a real flue gas oxygen content prediction is done with this designed application, and the result shows its accuracy and usefulness.
本文主要针对工矿不断变化的复杂工业过程,进行了基于半监督回归的在线和多模型软测量方法的研究,并运用仿真和实例证明了所研究方法的正确性和实用性。主要研究工作如下:
1.阐述了软测量技术的概念、步骤和建模方法;介绍了半监督技术、在线技术以及多模型技术的理论基础和发展情况。
2.提出了一种基于局部线性回归的半监督在线软测量方法。该方法以局部线性回归为基础,通过对目标函数引入未标记数据的方法,实现算法的半监督化,仿真证明了半监督算法可以有效地提高预测精度。针对算法中参数选择的问题,提出了一种半监督局部线性回归的自适应参数选择方法,该方法利用软测量主导变量的斜率估计值对算法中的高斯核宽进行自适应选择。最后,在此基础上,本文提出了一种基于滚动时间窗的半监督在线软测量方法,该方法能够使软测量模型实时更新,适应输入数据的变化,仿真结果证明了采用自适应参数的半监督在线算法拟合性更好。
3.本文对多模型软测量系统进行了研究。针对多工况、非线性强的工业过程,提出了两种基于半监督回归的多模型软测量系统建模方法:加权式多模型建模方法和切换式多模型建模方法。加权式多模型建模方法首先根据输入数据的特点建立多个类别的子模型进行预测,然后对输入数据进行模糊聚类分析,得到每个输入数据点对所有子模型的模糊隶属度,最后利用该模糊隶属度对所有子模型的预测结果进行加权计算,得到多模型的预测结果;切换式多模型建模方法首先对所有的输入数据进行分类,接着根据数据点类别的不同采用不同的子模型进行预测,并将预测结果直接作为多模型的预测结果。本文对这两种多模型方法均进行了仿真检验,结果表明多模型系统确实可以有效提高预测精度。
4.最后,设计了一套基于半监督回归的多模型软测量系统应用方案,该方案以超超临界机组为研究对象,烟气含氧量为主导变量,以及其他从工业生产中获得的十几组变量数值作为辅助变量。方案一共分为两个部分:离线部分和在线部分,本文对其中的步骤做了详细说明,比如工况分析、数据预处理等等。最后利用该方法对超超临界机组烟气含氧量进行了多模型软测量预测,预测结果证明了方法的正确性和实用性。
With development of modern society and demand of process technologies and environmental protection, industrial process has became more and more complex and is characterized by multi-operating conditions, time-variation and strong nonlinearity. In order to control processes accurately, process variables should be able to reflect the state of processes real-timely and accurately, therefore, the measurement of these variables is becoming an important part of industrial control system. If use the traditional method of sensor acquisition to measure these variables, the investment and maintenance costs are usually very high, and even there are delays in some variables’measurements, so sensor acquisition sometimes cannot reflect the changes of processes in real time. Nowadays, soft sensor becomes an effective way to solve the problem of these variables’measurements. For those non-linear and multi-state industrial processes whose operating conditions often change, a single static model usually cannot reflect the actual situation of the process correctly, but if combine soft sensor technology with online modeling technology and multi-modeling technology, the accuracy and reliability of soft sensor measurement can be improved effectively.
In this paper, a research of online and multi-model soft sensor system basing on semi-supervised regression is done for those complex industrial processes whose operating conditions often change. Simulations and application have shown correctness and practicality of this research. All of the work is listed as below:
1. Elaborated on concepts of soft sensor technology’s procedures and modeling methods; introduced semi-supervised technology, online technology and multi-model technology and their developments.
2. Proposed an online soft sensor modeling method basing on semi-supervised Local Linear Regression. This method makes Local Linear Regression become a semi-supervised method by importing unlabeled data to its objective function, and simulations proved the semi-supervised method can improve the system’s accuracy successfully. For parameters’selecting, this paper proposed an adaptive method which selects Gaussian kernel width by dominate variable’s estimated slope in semi-supervised Local Linear Regression. At last, basing on the above research, this paper used rolling time window to propose an online method of SSLLR, and this method makes soft sensor model can be updated in real time and be able to adaptive to input data, simulation result shows that the adaptive parameter selection method makes SSLLR have a better prediction.
3. Researched on multi-model soft sensor modeling. For those industrial processes which have multi-operating conditions and strong nonlinearity, this paper proposed two multi-model soft sensor modeling methods basing on semi-supervised regression: weighted multi-modeling method and switched multi-modeling method. Weighted multi-modeling method uses all sub-models which are created by input data’s characteristic to predict the dominate variable at first, then gets fuzzy membership degrees of all input data by fuzzy clustering method, and uses these membership degrees as weights to compute multi-model prediction result by all sub-models’predictions at last; Switched multi-modeling method clusters all input data into different categories at first, and uses only one specific sub-model to predict the dominate variable, and this sub-model is determined by input data’s characteristic, finally makes the sub-model prediction result be the multi-model prediction result directly. Both of the two multi-modeling methods are simulated in this paper, and the results show that multi-model can really improve the accuracy of prediction.
4. At last, this paper designed a multi-model soft sensor system’s application based on semi-supervised regression. The application focus on ultra-supercritical units, and flue gas oxygen content is chosen as the dominate variable, besides, a dozen sets of variables which are obtained from industrial production are chosen as the auxiliary variable. The application is designed into two parts: offline part and online part. Detailed descriptions of these steps, such as working conditions analysis, data preprocessing, are illustrated in this paper. At last, a real flue gas oxygen content prediction is done with this designed application, and the result shows its accuracy and usefulness.
引文
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[3] Solihin M, Wahyudi D. Development of soft sensor for sensorless automatic gantry crane using RBF neural networks[J]. Cybernetics and Intelligent Systems, 2006, 7(9):1-6
[4] Badhe Y, Tambe S, Kulkarni D. Soft-sensor development for fed-batch bioreactors using support vector regression[J]. Biochemical Engineering Journal, 2006, 27(3):225-239
[5] Nybom H, Kortelainen J, Jakobsson J. Freeness soft sensor in TMP-refining[C]. Control Systems 2004 Conference, 2004: 227-311
[6] Arthur K. Robust soft sensors on integration of genetic programming analytical neural networks and support vector machines Evolutionary Computation[C]. Proceedings of the 2002 Congress on Volume 1: 12-17, 2002: 896-901
[7] McAvoy T. J. Contemplative stance for chemical process control[C]. An IFAC report, Automatica, 1992, 28(2):441-442
[8]仲蔚.软测量与先进控制策略研究及其在石油化工过程中的应用[博士学位论文].上海:华东理工大学,1999
[9]潘立登,李大字,马俊英.软测量技术原理与应用[M].北京:中国电力出版社,2008
[10]俞金寿.软测量技术及其应用[J].自动化仪表,2008,29:1-7
[11]李海青.软测量技术原理及应用[M].北京:化工出版社,2000
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