帶時滯估計的軟測量建模方法研究
發(fā)布時間：2018-09-03 07:13
【摘要】：化工過程對象呈現(xiàn)顯著的非線性和時變性,為了對過程實施高效的監(jiān)控策略,廣泛以軟測量技術的手段對反映產(chǎn)品質(zhì)量指標的難測變量(即主導變量)進行推斷估計。如今,隨著過程工況復雜度的日益增加,工業(yè)界對于軟測量技術的精度和可靠性方面的要求也相應提高。在實際過程中,主導變量的獲取通常受到裝置成本、儀表可靠性或技術瓶頸等方面的限制,存在很大的測量滯后性。盡管軟測量建模領域的研究不斷邁向自適應時代,時滯信息卻往往不被考慮在建模過程中。為了進一步改善傳統(tǒng)軟測量建模方法的預測精度,本論文不僅針對工業(yè)過程的時變和非線性特征,同時還考慮了過程數(shù)據(jù)集中隱含的時滯信息,在現(xiàn)有的軟測量技術研究成果的基礎上,以時間差高斯過程回歸算法為基礎,對帶時滯估計的自適應軟測量建模方法進行了研究。全文的主要研究內(nèi)容如下:1.針對建模數(shù)據(jù)時序匹配不一致和變量漂移的問題,提出一種基于模糊曲線分析(Fuzzy Curve Analysis,FCA)的時間差高斯過程回歸(Time Difference Gaussian Process Regression,TDGPR)建模方法。該方法利用離線估計的時滯參數(shù)重新匹配建模樣本時序,對于查詢樣本,采用TDGPR模型對主導變量進行在線預測。2.針對傳統(tǒng)全局時間差(Time Difference,TD)模型的“老化”問題,基于選擇性集成思想,提出一種局部時間差高斯過程回歸(Local Time Difference Gaussian Process Regression,LTDGPR)的自適應建模方法。首先,對數(shù)據(jù)庫中的時滯動態(tài)信息進行挖掘,并利用該信息對建模數(shù)據(jù)進行重構;然后,采取局部化策略對差分后的重構樣本進行統(tǒng)計劃分,得到LTDGPR模型集。對于查詢樣本,在線選擇部分泛化能力強的LTDGPR子模型進行集成,估計出含一定時間差的主導變量動態(tài)偏移值;最后,基于TD模型思想對主導變量值進行實時預測。3.考慮到過程非線性和時滯呈現(xiàn)出的階段性特征,提出一種基于局部時滯重構(Local Time-delay Reconstruction,LTR)的滑動窗時間差高斯過程回歸(Moving Window Time Difference Gaussian Process Regression,MWTDGPR)建模方法。該方法以滑動窗和TD組合策略的方式逐步跟蹤過程局部非線性突變和緩變特征,同步實現(xiàn)了局部樣本集的時序校正和重構,為其它的時滯非線性時變化工過程建模問題提供了一種可行的框架。論文通過實際過程的數(shù)據(jù)仿真研究驗證了上述方法的可行性和精度,仿真結(jié)果充分顯示考慮時滯估計的自適應軟測量建模對于化工過程的經(jīng)濟效益和安全平穩(wěn)運行具有重大意義。
[Abstract]:Chemical process objects show significant nonlinear and time-varying characteristics. In order to implement an efficient monitoring strategy, the difficult variables (i.e. dominant variables) which reflect the product quality index are inferred and estimated by soft sensing technology. Nowadays, with the increasing complexity of the process, the requirements of the industry for the accuracy and reliability of soft sensing technology are also increased. In the actual process, the acquisition of dominant variables is usually limited by the cost of the device, the reliability of the instrument or the technical bottleneck, etc., so there is a great lag in measurement. Although the research in the field of soft sensor modeling is moving towards the era of adaptation, time-delay information is often not considered in the modeling process. In order to further improve the prediction accuracy of traditional soft sensor modeling methods, this paper not only aims at the time-varying and nonlinear characteristics of industrial processes, but also takes into account the implicit time-delay information in the process data set. Based on the existing research results of soft sensing technology and based on the time difference Gao Si process regression algorithm, the adaptive soft sensor modeling method with time delay estimation is studied. The main contents of this paper are as follows: 1. Aiming at the inconsistency of time series matching and variable drift of modeling data, a time difference Gao Si process regression (Time Difference Gaussian Process Regression,TDGPR (Time Difference Gaussian Process Regression,TDGPR) modeling method based on fuzzy curve analysis (Fuzzy Curve Analysis,FCA) is proposed. In this method, the time series of modeling samples is rematched by off-line estimation of time-delay parameters. For query samples, the TDGPR model is used to predict the dominant variables online. Aiming at the problem of "aging" of traditional global time difference (Time Difference,TD) model, an adaptive modeling method of local time-difference Gao Si process regression (Local Time Difference Gaussian Process Regression,LTDGPR) is proposed based on the idea of selective integration. Firstly, the time-delay dynamic information in the database is mined, and the modeling data is reconstructed using this information. Then, the LTDGPR model set is obtained by statistical partitioning of the reconstructed samples after the difference by using the localization strategy. For the query samples, the LTDGPR submodel with strong generalization ability is selected online to integrate to estimate the dynamic offset value of the dominant variable with a certain time difference. Finally, based on the idea of TD model, the dominant variable value is predicted in real time. 3. Considering the stage characteristics of process nonlinearity and time-delay, a sliding window time-difference Gao Si process regression (Moving Window Time Difference Gaussian Process Regression,MWTDGPR modeling method based on local time-delay reconstruction (Local Time-delay Reconstruction,LTR) is proposed. The method uses sliding window and TD combination strategy to track the local nonlinear mutation and slowly varying feature step by step, and synchronously realizes the timing correction and reconstruction of the local sample set. It provides a feasible framework for other nonlinear time-varying chemical process modeling problems with time delay. The feasibility and accuracy of the above method are verified by the data simulation of the actual process. The simulation results show that the adaptive soft-sensor modeling with time-delay estimation is of great significance for the economic benefit and safe and stable operation of chemical process.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TQ018
本文編號：2219287
[Abstract]:Chemical process objects show significant nonlinear and time-varying characteristics. In order to implement an efficient monitoring strategy, the difficult variables (i.e. dominant variables) which reflect the product quality index are inferred and estimated by soft sensing technology. Nowadays, with the increasing complexity of the process, the requirements of the industry for the accuracy and reliability of soft sensing technology are also increased. In the actual process, the acquisition of dominant variables is usually limited by the cost of the device, the reliability of the instrument or the technical bottleneck, etc., so there is a great lag in measurement. Although the research in the field of soft sensor modeling is moving towards the era of adaptation, time-delay information is often not considered in the modeling process. In order to further improve the prediction accuracy of traditional soft sensor modeling methods, this paper not only aims at the time-varying and nonlinear characteristics of industrial processes, but also takes into account the implicit time-delay information in the process data set. Based on the existing research results of soft sensing technology and based on the time difference Gao Si process regression algorithm, the adaptive soft sensor modeling method with time delay estimation is studied. The main contents of this paper are as follows: 1. Aiming at the inconsistency of time series matching and variable drift of modeling data, a time difference Gao Si process regression (Time Difference Gaussian Process Regression,TDGPR (Time Difference Gaussian Process Regression,TDGPR) modeling method based on fuzzy curve analysis (Fuzzy Curve Analysis,FCA) is proposed. In this method, the time series of modeling samples is rematched by off-line estimation of time-delay parameters. For query samples, the TDGPR model is used to predict the dominant variables online. Aiming at the problem of "aging" of traditional global time difference (Time Difference,TD) model, an adaptive modeling method of local time-difference Gao Si process regression (Local Time Difference Gaussian Process Regression,LTDGPR) is proposed based on the idea of selective integration. Firstly, the time-delay dynamic information in the database is mined, and the modeling data is reconstructed using this information. Then, the LTDGPR model set is obtained by statistical partitioning of the reconstructed samples after the difference by using the localization strategy. For the query samples, the LTDGPR submodel with strong generalization ability is selected online to integrate to estimate the dynamic offset value of the dominant variable with a certain time difference. Finally, based on the idea of TD model, the dominant variable value is predicted in real time. 3. Considering the stage characteristics of process nonlinearity and time-delay, a sliding window time-difference Gao Si process regression (Moving Window Time Difference Gaussian Process Regression,MWTDGPR modeling method based on local time-delay reconstruction (Local Time-delay Reconstruction,LTR) is proposed. The method uses sliding window and TD combination strategy to track the local nonlinear mutation and slowly varying feature step by step, and synchronously realizes the timing correction and reconstruction of the local sample set. It provides a feasible framework for other nonlinear time-varying chemical process modeling problems with time delay. The feasibility and accuracy of the above method are verified by the data simulation of the actual process. The simulation results show that the adaptive soft-sensor modeling with time-delay estimation is of great significance for the economic benefit and safe and stable operation of chemical process.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TQ018
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