基于混合高斯回歸的動態(tài)軟測量方法研究
發(fā)布時間：2018-09-13 06:43
【摘要】：工業(yè)過程中,許多關鍵變量難以在線測量。軟測量技術成為解決該問題的關鍵技術之一。目前,許多模型是基于測量過程處于穩(wěn)態(tài)工況的假設,故模型大多為靜態(tài)軟測量模型。由于生產過程中工藝改變、物理結構特性變化以及環(huán)境等因素的影響使得生產過程通常處于動態(tài)工況,僅考慮對象靜態(tài)特性是不夠的,有必要對動態(tài)軟測量建模方法進行深入研究。本文以青霉素仿真過程和工業(yè)紅霉素發(fā)酵過程為對象,采用高斯過程回歸以及混合高斯回歸方法來研究建立動態(tài)軟測量模型。具體工作如下:(1)針對靜態(tài)軟測量模型預測精度低和魯棒性差等問題,本文提出了一種基于多準則和高斯過程回歸的動態(tài)軟測量建模方法。該方法綜合考慮多種模型定階準則,提出了高斯過程回歸動態(tài)軟測量模型定階策略,為模型階數確定提供了依據,并將所提動態(tài)軟測量模型應用于紅霉素發(fā)酵過程的生物量濃度估計。研究結果表明,基于高斯過程回歸的動態(tài)軟測量建模方法可以實現對生物量濃度的高精度預測,且預測結果具有較小的置信度區(qū)間。(2)針對單高斯過程回歸動態(tài)軟測量模型的局限性,提出了一種混合高斯回歸動態(tài)軟測量模型。該模型有兩個重要參數,即高斯元的個數和模型階數,為了獲得優(yōu)化軟測量模型,提出一個迭代策略優(yōu)化兩個結構參數。將所提出的動態(tài)混合高斯回歸軟測量模型應用于青霉素仿真過程和工業(yè)紅霉素發(fā)酵過程的生物量濃度估計,并和現有的動態(tài)高斯過程回歸軟測量模型比較。結果表明,所提動態(tài)混合高斯回歸軟測量模型具有較高的預測精度,更適合于動態(tài)多相/多模態(tài)發(fā)酵過程。(3)針對移動窗口算法和混合高斯回歸軟測量模型中移動窗口算法的大量更新,降低了模型的計算效率,并占用大量的計算機內存資源等問題。本文提出了一種基于模型性能評估的遞推混合高斯回歸建模方法來減少遞推混合高斯回歸建模方法的模型校正頻率。首先,根據過程初始特性自動生成模型的初始置信限。然后,將預測均方根誤差指標作為評價模型的標準。根據模型的性能評估結果,選擇性地激活模型校正,同時更新置信限。最后,將開發(fā)的模型用于青霉素仿真過程和工業(yè)紅霉素發(fā)酵過程的生物量濃度軟測量。仿真結果表明,所開發(fā)的模型大大提高了計算效率(模型校正頻率大大降低),而預測精度的損失可以忽略不計,且與混合高斯回歸模型相比,預測精度明顯提高。
[Abstract]:In the industrial process, many key variables are difficult to measure online. Soft sensing technology has become one of the key technologies to solve this problem. At present, many models are based on the assumption that the measurement process is in steady state, so most of the models are static soft sensor models. Due to the influence of process change, physical structure characteristic change and environment factors, the production process is usually in dynamic condition, so it is not enough to consider the static characteristics of the object. It is necessary to study the modeling method of dynamic soft sensor. In this paper, the simulation process of penicillin and the fermentation process of industrial erythromycin were used to establish dynamic soft sensor model by using Gao Si process regression and mixed Gao Si regression method. The main works are as follows: (1) aiming at the problems of low prediction accuracy and poor robustness of the static soft-sensor model, a dynamic soft-sensor modeling method based on multi-criteria and Gao Si process regression is proposed in this paper. This method synthetically considers several model order determination criteria, and puts forward the order determination strategy of Gao Si process regression dynamic soft sensor model, which provides the basis for determining the model order. The dynamic soft sensor model was applied to estimate the biomass concentration of erythromycin fermentation process. The results show that the dynamic soft sensor modeling method based on Gao Si process regression can achieve high precision prediction of biomass concentration. The prediction results have a small confidence range. (2) in view of the limitation of single Gao Si regression dynamic soft sensor model, a hybrid Gao Si regression dynamic soft sensor model is proposed. The model has two important parameters, namely, the number of Gao Si elements and the order of the model. In order to obtain the optimized soft sensor model, an iterative strategy is proposed to optimize the two structural parameters. The dynamic mixed Gao Si regression soft sensor model was applied to estimate the biomass concentration of penicillin simulation process and industrial erythromycin fermentation process. The results show that the proposed dynamic mixed Gao Si regression soft sensor model has high prediction accuracy. It is more suitable for dynamic multiphase / multimodal fermentation process. (3) aiming at a large number of updates of moving window algorithm and mixed Gao Si regression soft sensor model, the computational efficiency of the model is reduced. And occupy a large number of computer memory resources and other problems. In this paper, a recursive mixed Gao Si regression modeling method based on model performance evaluation is proposed to reduce the frequency of model correction of recursive mixed Gao Si regression modeling method. First, the initial confidence limits of the model are automatically generated according to the initial characteristics of the process. Then, the root-mean-square error index is taken as the criterion of the evaluation model. Based on the performance evaluation of the model, the model correction is selectively activated and the confidence limit is updated. Finally, the developed model is applied to the soft measurement of biomass concentration in penicillin simulation process and industrial erythromycin fermentation process. The simulation results show that the developed model greatly improves the calculation efficiency (the calibration frequency of the model is greatly reduced), and the loss of prediction accuracy can be negligible, and compared with the mixed Gao Si regression model, the prediction accuracy is obviously improved.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O212.1
本文編號：2240385
[Abstract]:In the industrial process, many key variables are difficult to measure online. Soft sensing technology has become one of the key technologies to solve this problem. At present, many models are based on the assumption that the measurement process is in steady state, so most of the models are static soft sensor models. Due to the influence of process change, physical structure characteristic change and environment factors, the production process is usually in dynamic condition, so it is not enough to consider the static characteristics of the object. It is necessary to study the modeling method of dynamic soft sensor. In this paper, the simulation process of penicillin and the fermentation process of industrial erythromycin were used to establish dynamic soft sensor model by using Gao Si process regression and mixed Gao Si regression method. The main works are as follows: (1) aiming at the problems of low prediction accuracy and poor robustness of the static soft-sensor model, a dynamic soft-sensor modeling method based on multi-criteria and Gao Si process regression is proposed in this paper. This method synthetically considers several model order determination criteria, and puts forward the order determination strategy of Gao Si process regression dynamic soft sensor model, which provides the basis for determining the model order. The dynamic soft sensor model was applied to estimate the biomass concentration of erythromycin fermentation process. The results show that the dynamic soft sensor modeling method based on Gao Si process regression can achieve high precision prediction of biomass concentration. The prediction results have a small confidence range. (2) in view of the limitation of single Gao Si regression dynamic soft sensor model, a hybrid Gao Si regression dynamic soft sensor model is proposed. The model has two important parameters, namely, the number of Gao Si elements and the order of the model. In order to obtain the optimized soft sensor model, an iterative strategy is proposed to optimize the two structural parameters. The dynamic mixed Gao Si regression soft sensor model was applied to estimate the biomass concentration of penicillin simulation process and industrial erythromycin fermentation process. The results show that the proposed dynamic mixed Gao Si regression soft sensor model has high prediction accuracy. It is more suitable for dynamic multiphase / multimodal fermentation process. (3) aiming at a large number of updates of moving window algorithm and mixed Gao Si regression soft sensor model, the computational efficiency of the model is reduced. And occupy a large number of computer memory resources and other problems. In this paper, a recursive mixed Gao Si regression modeling method based on model performance evaluation is proposed to reduce the frequency of model correction of recursive mixed Gao Si regression modeling method. First, the initial confidence limits of the model are automatically generated according to the initial characteristics of the process. Then, the root-mean-square error index is taken as the criterion of the evaluation model. Based on the performance evaluation of the model, the model correction is selectively activated and the confidence limit is updated. Finally, the developed model is applied to the soft measurement of biomass concentration in penicillin simulation process and industrial erythromycin fermentation process. The simulation results show that the developed model greatly improves the calculation efficiency (the calibration frequency of the model is greatly reduced), and the loss of prediction accuracy can be negligible, and compared with the mixed Gao Si regression model, the prediction accuracy is obviously improved.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O212.1
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