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On-line batch process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

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Journal of Shanghai Jiaotong university(Science) 2006年第3期11卷 307-312,316页

作者：赵旭邵惠鹤 Dept.of Automation Shanghai Jiaotong Univ.Shanghai 200030China

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

关键词： batch process on-line process monitoring fault diagnosis Fisher discriminant analysis (FDA) multiway principal component analysis (MPCA)

Consequence Identification for Maloperation in batch process

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Chinese Journal of Chemical Engineering 2013年第12期21卷 1347-1359页

作者：张玉良张贝克马昕曹柳林吴重光 College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China

batch processes are important in chemical industry,in which operators usually play a major role and hazards may arise by their inadvertent *** this paper,based on hazard and operability study and concept of qualitative simulation,an automatic method for adverse consequence identification for potential maloperation is *** qualitative model for production process is expressed by a novel directed *** operation deviations from normal operating procedure are identified systematically by using a group of *** proposed algorithm is used for qualitative simulation of batch processes to identify the effects of *** method is illustrated with a simple batch process and a batch reaction *** results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including *** analysis for possible plant maloperations,some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.

关键词： maloperation consequence identification batch process hazard and operability study qualitative simulation

batch process Control from Practice to 2D Model Predictive Control

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Batch Process Control from Practice to 2D Model Predictive C...

2009中国控制与决策会议

作者： Ke Yao,Yi Yang,Furong Gao~* Department of Chemical and Biomolecular Engineering,The Hong Kong University of Science & Technology,Hong Kong

Owing to the natures of batch processes,such as high nonlinearity,time-varying,and limited batch time duration,their control remains as a challenge to modern *** paper takes a typical batch process,injection molding,as an example to present a set of control schemes for batch processes. Advanced control algorithms such as adaptive control and model predictive control have been adopted to deal with the inherent process nonlinear and time-varying *** control algorithms are all focused on single cycle control performance.A multi-cycle two-dimensional model predictive learning control has been developed for batch processes control to take advantages of batch process repeatability. In this presentation,besides showing the control results/methods,the authors wish to illustrate the development evolution with their understanding of the natures of batch processes in general,injection molding in particular.

关键词： batch process Injection Molding 2D Model Predictive Control Iterative Learning

batch process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder

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Batch Process Monitoring Based on Multi-stage Fourth Order M...

第32届中国控制与决策会议

作者： Jin Chen Wang Pu Wang Kai Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked Autoencoder（M-FOM-SAE） is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： batch process Fault monitoring non-Gaussian Multi-stage Stacked Autoencoder

Design and Analysis of Integrated Predictive Iterative Learning Control for batch process Based on Two-dimensional System Theory

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Chinese Journal of Chemical Engineering 2014年第7期22卷 762-768页

作者：陈宸熊智华钟宜生 Department of Automation Tsinghua University

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

关键词： lterative learning control Model predictive control Integrated control batch process Two-dimensional systems

Fault diagnosis of batch process based on improved GA-SeparableConv1D-TCN network

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Fault diagnosis of batch process based on improved GA-Separa...

第43届中国控制会议

作者： He Yueyang Liang Xiuxia Xia Manman Liang Tao School of Artificial Intelligence and Data Science Hebei University of Technology

batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency,it is widely used in biopharmaceutical,wastewater treatment,fine chemical and other fields. Due to the improvement of modern equipment and the expansion of the scale of the factory,the chance of accidents and failures also showed an exponential increase trend. Therefore,fault diagnosis is very important to ensure the stability and safety of chemical processes. In order to improve the safety and reliability of batch process,an improved Genetic Algorithm is proposed to optimize the parameters of Separable Convolution Network and Temporal Convolutional Network(SeparableConv1D-TCN) for fault diagnosis of batch process. Firstly,the SeparableConv1D-TCN network is constructed to extract features from the original intermittent data and perform fault diagnosis by standardizing the intermittent data. In order to find the network parameters with high diagnostic accuracy,the genetic algorithm with good point set method and optimal domain search is used to optimize. The effectiveness of the method is verified by simulation experiments and comparative experiments with penicillin experimental data.

关键词： batch process Fault Diagnosis SeparableConv1D-TCN Genetic Algorithm

Unsupervised Feature Transfer for batch process Based on Geodesic Flow Kernel

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Unsupervised Feature Transfer for Batch Process Based on Geo...

第32届中国控制与决策会议

作者： Zheming Zhang Fang Wang Yusong Pang Gaowei Yan College of Electrical and Power Engineering Taiyuan University of Technology College of Mechanical Maritime and Materials EngineeringDelft University of Technology

The problem of misalignment of the original measurement model is caused by nonlinear,time-varying characteristic of the batch *** this paper,a method based on geodesic flow kernel（GFK） for feature transfer is *** mapping data into the manifold space,the feature transfer from source domain to target domain is *** adaptation of real-time data and modeling data is performed to reduce the distribution difference between *** historical data through distribution adaptation is used to establish a regression model to predict the real-time data,by which the unsupervised batch process soft sensor modeling is *** application of predicting the concentration of penicillin between different batches during the fermentation of penicillin demonstrated that the prediction accuracy of the model can be improved more effectively than the traditional soft sensor method.

关键词： batch process geodesic flow kernel unsupervised feature transfer penicillin

Enhanced batch process Monitoring and Quality Prediction Using Multi-phase Dynamic PLS

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Enhanced Batch Process Monitoring and Quality Prediction Usi...

第三十届中国控制会议

作者： QI Yongsheng~(1,2),WANG Pu~1,GAO Xuejin~1 1.College of Electronic Information and Control Engineering,Beijing University of Technology,Beijing 100124,P.R.China 2.College of Electric Power,Inner Mongolia University of Technology,Huhhot,010051,P.R.China

＜正＞In industrial manufacturing,most batch processes are multi-phase and uneven-length batch processes in nature, phase-based approaches are intuitively well suited for batch process monitoring and quality *** this paper,a new strategy is proposed using multi-phase dynamic partial least squares（DPLS） for batch processes monitoring and quality ***,batch process data was automatically divided into several phases using Gaussian mixture model（GMM） clustering *** run-to-run variations among different instances of a phase are synchronized by using dynamic time warping（DTW）.Finally,multi-phase DPLS model is built between each phase and the quality *** proposed method easily handles the following problems:（1）static single model;（2）process and its model do not match;（3） linear method may not be efficient in compressing and extracting dynamic nonlinear process *** idea and algorithm are illustrated with respect to the typical data collected from a benchmark simulation of fed-batch penicillin fermentation production. The simulation results demonstrate the effectiveness of the proposed method in comparison to original DPLS.

关键词： batch process Dynamic PLS Gaussian Mixture Model Quality Prediction

Trajectory Function based soft sensor for quality prediction of complex batch process

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Trajectory Function based soft sensor for quality prediction...

第32届中国过程控制会议(CPCC2021)

作者： Jingxiang Liu Junghui Chen Jie Hou School of Marine Electrical Engineering Dalian Maritime University Department of Chemical Engineering Chung-Yuan Christian University College of Automation Chongqing University of Posts and Telecommunications

batch processes have found great applications in many industrials to obtain high value-added products,providing more requirements and challenges to monitor and predict the final product *** the existing data-driven modelling methods,the measured three-dimensional(3-D) batch data are often unfolded into two-dimensional(2-D) matrix,resulting in raw data structure *** grasp the continuous property in the variables’ trajectories and facilitate the quality prediction performance of the complex industrial batch processes,such as copolymer emulsion processes,a novel soft sensor method named wavelet functional partial least squares is proposed in this *** this proposed functional method,the 3-D batch data can be transferred into 2-D functional *** functional data analysis method is performed on the functional matrix directly,with the natural advantages on handling nonlinear and uneven-length *** merit of the proposed method is finally demonstrated by an industrial copolymer emulsion process.

关键词： Functional soft sensor wavelet functional partial least squares uneven-length problem nonlinear soft sensor batch process