Predictive on-line monitoring of continuous processes

Multi-Block Predictive Monitoring of Continuous Processes

Multivariate statistical methods such as principal component analysis (PCA) and partial least square (PLS) have been successfully used in modeling multivariable continuous processes (Kaspar and Ray, 1992; Kourti and MacGregor, 1995; Chen and McAvoy, 1998). Several extensions of the conventional PCA/PLS to batch processes have also been reported, among which multiway PCA (MPCA) model is the most widely used (Wold et al., 1987; Nomikos and MacGregor, 1994, 1995; Wise et al., 1999; Smilde, 2001). The MPCA model is ill-suited for multistage batch processes, as it takes the entire batch data as a single object, and it is difficult to reveal the changes of process correlation from stage to stage. Considering that the multiplicity of the operation stage is an inherent nature of many batch processes, each stage has its own underlying characteristics and the process can exhibit significantly different behaviors over different operation stages; it is desirable to develop a stage-based model that can reflect the inherent process stage nature to improve the process understanding and monitoring efficiency. Kosanovich et al. (1994) and Dong and McAvoy (1995) developed two MPCA/nonlinear MPCA models, utilizing the two-stage nature of a jacketed exothermic batch chemical reactor. Their results show that the two-stage models are more powerful than a single model. Their stage models, however, inherit the common weakness of the MPCA model that the unavailable future data in an evolving batch should be estimated for on-line monitoring. A new stage-based sub-PCA modeling method is proposed in this article for multistage batch processes, based on the recognition of the following: (1) a batch process may be divided into several “operation” stages reflecting its inherent process correlation nature; (2) despite that the process may be time varying, the correlation of its variables will be largely similar within the same “operation” stage. Changes in the correlation may be used to indicate changes in the process “operation” stages. We have placed a quotation mark around “operation” to indicate that the operation referred to in this article may not, and does not have to, have the exact correspondence to the physical operations of the process. Based on the above recognition, a representative model can be built for each stage, using the conventional two-way PCA model. This allows two-way PCA to be “directly” applied to a batch process after a proper stage division; a stage division algorithm is also developed in the article. A three-tank process, as an experimental verification system, is finally introduced to illustrate the effectiveness of the proposed.

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

In this paper, we introduce a new framework for classification of short duration voltage reductions in the area of Power Quality Monitoring using Multiway Principal Component Analysis (MPCA). Firstly, we recast the sags occurred in High Voltage (HV) and Medium Voltage (MV) lines in a format which is suitable for MPCA. Then, MPCA technique is employed for building statistical models for classification of sags originated in HV and MV networks and recorded in the same substation. Projecting sags registered in different substations to MPCA models of other substations has been also explored to deduce similarities and dissimilarities between different substations according to the sags registered in them.

With the development of modernization, the application of seamless tube becomes widespread. As the first process of seamless tube, piercing is vital for the quality of the tube. The solid round billet will be transformed into a hollow shell after the piercing process. The defects of hollow shell cannot be cleared in the following process, so a monitoring model for the quality of the hollow shell is important. But the piercing process is very complicated, and a mechanism model is difficult to build between the qualities of the hollow shell and measurement variables. Furthermore, an intelligent model is needed. We established two piercing process monitoring and fault diagnosis models based on the multiway principal component analysis (MPCA) model and the multistage MPCA model, respectively, and furthermore we made a comparison between these two concepts. We took three ways to divide the period based on process,K-means, and GA, respectively. Simulation experiments have shown that the multistate MPCA method has advantage over the MPCA method and the model based on the genetic algorithm (GA) can monitor the process effectively and detect the faults.

Being a widely used technology in papermaking industry, the fault diagnosis of the sequence batch reactor (SBR) wastewater treatment process has been a significant challenge owing to the batch characteristics. In order to decrease the complexity of monitoring the SBR process, the inherent multi–period characteristics has been considered in this study. The conventional multi– way principal component analysis (MPCA) method has been improved with sub–period division strategies (Sub–MPCA) to diagnose the faults in the SBR process. Aiming at identify the most applicative sub–period division strategy for the SBR process, four types of strategies (Scenarios 1–4) have been tested and compared. Beginning with the off–line modeling, the training set data was used to motivate the sub–MPCA models and acquire the control limits of T2 and SPE statistics. Subsequently, the fault alarm rates (FARs) of the developed models were estimated to verify the models. Finally, the fault diagnosis performances of the models were evaluated with the testing data set from an abnormal batch. After the examinations of the four sub–period division strategies in SBR process, the result revealed that a multi–sub–period algorithm based on the similarities of the loading matrices between the adjacent time slices (Scenario 3) demonstrated the best performance with fewer diagnostic error, which was identified the most accurate model for the fault diagnosis in the SBR wastewater treatment process.

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

On-line batch process monitoring using a consecutively updated multiway principal component analysis model

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

MODELLING INDUSTRIAL FERMENTATION DATA WITH MULTIWAY MULTIVARIATE TECHNIQUES

Study of the application of multiway multivariate techniques to model data from an industrial fermentation process

Trajectory tracking in batch processes using latent variable models

This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided.

Batch processes are very important in most industries and are used to produce high-value-added products, which cause their monitoring and control to emerge as essential techniques. In this paper, a new method was developed based on Kalman filter(KF) and multiway kernel principal component analysis(MKPCA) for on-line batch process monitoring. Three-way batch data of normal batch process are unfolded batch-wise. Then KPCA is used to capture the nonlinear characteristics within normal batch processes and set up the more accurate monitoring model of batch processes. The on-line monitoring uses a Kalman filter which can estimate the entire trajectory of the current batch run. Comparison of the monitoring performance of the method with that of the traditional multiway principal component analysis(MPCA) method on a benchmark fed-batch penicillin fermentation process shows that the proposed method had better monitoring performance, and that fewer false alarms and small fault detection delay were obtained. In both off-line analysis and on-line batch monitoring, the proposed approach can effectively extract the nonlinear relationships among the process variables.

On-line monitoring of batch processes using generalized additive kernel principal component analysis