發(fā)布時間：2018-12-12 08:02

【摘要】：間歇過程廣泛應(yīng)用于各類生產(chǎn)行業(yè),在國民經(jīng)濟(jì)發(fā)展中發(fā)揮越來越大的作用。但受限于傳感檢測技術(shù),關(guān)鍵變量難以實現(xiàn)在線檢測,且間歇過程具有高度的非線性、非高斯性和時變性,導(dǎo)致生產(chǎn)過程的監(jiān)控優(yōu)化異常困難�；�于間歇過程的狀態(tài)空間模型,狀態(tài)估計方法能夠根據(jù)檢測值的統(tǒng)計規(guī)律,通過濾波的形式實現(xiàn)對關(guān)鍵變量的準(zhǔn)確估計。實際生產(chǎn)過程中,存在兩種檢測值:在線檢測值和離線檢測值,在線檢測值無時滯但精度低,離線檢測值有時滯但精度高。另一方面,典型的間歇過程具有周期性批量生產(chǎn)的特點,批次較多。考慮時滯檢測值的存在和多批次特性,本文關(guān)于間歇過程狀態(tài)估計的研究內(nèi)容如下:(1)針對間歇過程中關(guān)鍵變量,在線檢測精度低、離線檢測時滯大的問題,基于貝葉斯方法提出一種融合時滯檢測值信息的狀態(tài)估計方法。鑒于在線和離線檢測值的采樣周期不同,分僅有在線檢測值和兩種檢測值并存等兩種情況進(jìn)行分析。該算法以粒子濾波算法為基礎(chǔ),并基于貝葉斯方法對其進(jìn)行擴展,實現(xiàn)兩種檢測值的信息融合。數(shù)字仿真和生物制氫過程的實驗結(jié)果表明,該方法能夠較好地處理含時滯檢測值的狀態(tài)估計問題,且效果優(yōu)于不考慮時滯檢測值的情況。(2)針對間歇過程的多批次特性,建立時間維度及批次維度的雙維狀態(tài)空間模型,同時考慮時滯檢測值的信息,提出一種基于雙維狀態(tài)空間模型的時滯狀態(tài)估計算法。該算法通過貝葉斯方法及前/后向平滑融合先前批次和時滯檢測值的信息,估計效果隨批次維度增加而提高,且考慮時滯檢測值的狀態(tài)估計效果優(yōu)于不考慮時滯檢測值時的情況。在數(shù)字仿真和生物制氫過程中的仿真應(yīng)用驗證了該算法的有效性。(3)針對雙維狀態(tài)空間模型難以準(zhǔn)確建立的問題,結(jié)合間歇過程的多批次特性及重復(fù)特性,提出一種基于迭代學(xué)習(xí)的時滯狀態(tài)估計算法。該算法根據(jù)先前批次相同采樣時刻的估計狀態(tài)及測量模型,估計該時刻的期望檢測值,并與真實檢測值作差得到跟蹤誤差,通過迭代此誤差來提高當(dāng)前批次的估計精度。對于時滯檢測值,同樣采用貝葉斯方法和前/后向平滑融合其信息。最后,通過數(shù)字仿真和生物制氫過程中的仿真應(yīng)用驗證了所提方法的有效性和實用性。
[Abstract]:Intermittent process is widely used in all kinds of production industries and plays a more and more important role in the development of national economy. However, due to the sensor detection technology, the key variables are difficult to realize on-line detection, and the batch process is highly nonlinear, non-Gao Si and time-varying, which makes the monitoring and optimization of the production process extremely difficult. Based on the state space model of batch process, the state estimation method can estimate the key variables accurately by filtering according to the statistical rule of detection value. In the actual production process, there are two kinds of detection values: the on-line detection value and the off-line detection value, the on-line detection value has no delay but the precision is low, the off-line detection value has the delay but the precision is high. On the other hand, the typical batch process has the characteristic of periodic batch production. Considering the existence of time-delay detection values and the characteristics of multiple batches, the research contents of state estimation for batch processes are as follows: (1) for the key variables in batch processes, the on-line detection accuracy is low, and the off-line detection delay is large. Based on Bayesian method, a state estimation method based on time-delay detection value information is proposed. In view of the different sampling periods of on-line and off-line detection values, only on-line detection values and two detection values coexist to analyze. This algorithm is based on particle filter algorithm and extends it based on Bayesian method to realize the information fusion of two detection values. The experimental results of digital simulation and biological hydrogen production process show that the proposed method can deal with the state estimation problem with time-delay detection value, and the effect is better than that without considering the time-delay detection value. (2) in view of the multi-batch characteristics of the batch process, Two dimensional state space models of time dimension and batch dimension are established, and a delay state estimation algorithm based on two dimensional state space model is proposed, considering the information of time delay detection value at the same time. Based on Bayesian method and forward / backward smoothing, the estimation effect is improved with the increase of batch dimension. The effect of state estimation with time delay detection value is better than that without time delay detection value. The simulation results in digital simulation and biological hydrogen production show that the algorithm is effective. (3) aiming at the problem that the two-dimension state space model is difficult to establish accurately, the multi-batch and repeated characteristics of batch process are combined. A delay state estimation algorithm based on iterative learning is proposed. According to the estimation state and the measurement model of the same sampling time in the previous batch, the algorithm estimates the expected detection value at that time, and the tracking error is obtained from the real detection value, and the estimation accuracy of the current batch is improved by iterating the error. Bayesian method and forward / backward smoothing are also used to fuse the time delay detection values. Finally, the effectiveness and practicability of the proposed method are verified by the digital simulation and the simulation application in the biological hydrogen production process.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN713

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本文編號：2374229