Three-way data analysis with time lagged window for on-line batch process monitoring

In this paper, on-line batch process monitoring is developed on the basis of the three-way data structure and the time-lagged window of process dynamic behavior. Two methods, DPARAFAC (dynamic parallel factor analysis) and DTri-PLS (dynamic trilinear partial least squares), are used here depending on the process variables only or on the process variables and quality indices, respectively. Although multivariate analysis using such PARAFAC (parallel factor analysis) and Tri-PLS (trilinear partial least squares) models has been reported elsewhere, they are not suited for practicing on-line batch monitoring owing to the constraints of their data structures. A simple modification of the data structure provides a framework wherein the moving window based model can be incorporated in the existing three-way data structure to enhance the detectability of the on-line batch monitoring. By a sequence of time window of each batch, the proposed methodology is geared toward giving meaningful results that can be easily connected to the current measurements without the extra computation for the estimation of unmeasured process variables. The proposed method is supported by using two sets of benchmark fault detection problems. Comparisons with the existing two-way and three-way multiway statistical process control methods are also included.     

Shape anomaly detection for process monitoring of a sequencing batch reactor

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. Classical methods for data validation are quantitative in nature and require either accurate process knowledge, large representative data sets, or both. In contrast, a small section of the fault diagnosis literature has focused on qualitative data and model representations. The major benefit of such methods is that imprecise but reliable results can be obtained under previously unseen process conditions. This work continues with a line of work focused on qualitative trend analysis which is the qualitative approach to data series analysis. An existing method based on shape-constrained spline function fitting is expanded to deal explicitly with discontinuities and is applied here for the first time for anomaly detection. An experimental test case and a comparison with the principal component analysis method bear out the benefits of the qualitative approach to process monitoring.     

On-line batch process monitoring using dynamic PCA and dynamic PLS models

Producing value-added products of high-quality is the common objective in industries. This objective is more difficult to achieve in batch processes whose key quality measurements are not available on-line. In order to reduce the variations of the product quality, an on-line batch monitoring scheme is developed based on the multivariate statistical process control. It suggests using the past measured process variables without real-time quality measurement at the end of the batch run. The method, referred to as BDPCA and BDPLS, integrates the time-lagged windows of process dynamic behavior with the principal component analysis and partial least square respectively for on-line batch monitoring. Like traditional MPCA and MPLS approaches, the only information needed to set up the control chart is the historical data collected from the past successful batches. This leads to simple monitoring charts, easy tracking of the progress in each batch run and monitoring the occurrence of the observable upsets. BDPCA and BDPLS models only collect the previous data during the batch run without expensive computations to anticipate the future measurements. Three examples are used to investigate the potential application of the proposed method and make a comparison with some traditional on-line MPCA and MPLS algorithms.     

Dynamic model-based batch process monitoring

An integrated framework consisting of a multivariate autoregressive (AR) model and multi-way principal component analysis (MPCA) is described for the monitoring of the performance of a batch process. After pre-processing the data, i.e., batch data unfolding, mean-centring and scaling, the data are then filtered using an AR model to remove the auto- and cross-correlation inherent within the pre-processed batch data. Model order is determined using Akaike information criterion and the model parameters are estimated through the application of partial least squares to attain a stable solution. MPCA is then applied to the residuals from the AR model. Three monitoring statistics are considered for the detection of the onset of process abnormalities in the batch process. The main advantage of the proposed approach is that it can monitor batch dynamics along the mean trajectory without the requirement to estimate future observed values. The proposed AR model-based approach is illustrated through its application to two polymerization processes. The case studies indicate that it gives better monitoring results in terms of sensitivity and time to fault detection than the approaches proposed by Nomikos and MacGregor [1994. Monitoring batch processes using multi-way principal components. A.I.Ch.E. Journal 40(8), 1361-1375] and Wold et al. [1998. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems 44, 331-340].     

Real-time monitoring of an industrial batch process

This paper describes the development of a real-time monitoring system for a batch process operated by Aroma and Fine Chemicals Limited. The process shares many similarities with other batch processes in that cycle times can vary considerably, instrumentation is limited and inefficient laboratory assays are required to determine the end-point of each batch. The aim of the work conducted in this study was to develop a data driven system to accurately identify the end-point of the batch. This information can then be used to reduce the overall cycle time of the process. Novel approaches based upon multivariate statistical techniques are shown to provide a soft sensor that can estimate the product quality throughout the batch and provide a long-term estimate of the likely cycle time. This system has been implemented on-line and initial results indicate that it offers potential to significantly reduce operating costs.     

Multivariate statistical process control charts for batch monitoring of transesterification reactions for biodiesel production based on near-infrared spectroscopy

This work describes an application of Multivariate Statistical Process Control to monitor soybean oil transesterification. For the development of multivariate control charts, near infrared spectra were acquired in-line during the evolution of ten batches under Normal Operating Conditions. They were then organized in a three-way array (batch í spectral variable í time). This structure was analysed by the two most commonly used approaches to develop batch monitoring schemes for handling such kind of data, referred to as Nomikos-MacGregor (NM) and Wold-Kettaneh-Friden-Holmberg (WKFH), respectively. To assess the performance of the approaches, eight test batches, during which specific interferences were induced, were manufactured. When applied for off-line monitoring, both NM and WKFH correctly pointed out such intentionally produced failures. On the other hand, concerning on-line monitoring, NM exhibited a better fault detection capability than WKFH. Contribution plots were found to highlight the spectral region mostly affected by the disturbances regardless of the modelling strategy resorted to.     

On-line batch process monitoring using hierarchical kernel partial least squares

In this paper, new monitoring approach, hierarchical kernel partial least squares (HKPLS), is proposed for the batch processes. The advantages of HKPLS are: (1) HKPLS gives more nonlinear information compared to hierarchical partial least squares (HPLS) and multi-way PLS (MPLS) and (2) a new batch process monitoring using HKPLS does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The proposed method is applied to the penicillin process and continuous annealing process and is compared with MPLS and HPLS monitoring results. Applications of the proposed approach indicate that HKPLS effectively capture the nonlinearities in the process variables and show superior fault detectability.     

Subspace identification for two-dimensional dynamic batch process statistical monitoring

Dynamics are inherent characteristics of batch processes, which may be not only within a batch, but also from batch to batch. Two-dimensional dynamic principal component analysis (2-D-DPCA) method [Lu, N., Yao, Y., Gao, F., 2005. Two-dimensional dynamic PCA for batch process monitoring. A.I.Ch.E. Journal 51, 3300-3304] can model both kinds of batch dynamics, but may lead to the inclusion of large number of lagged variables and make the contribution plot difficult to read. To solve this problem, subspace identification technique is combined with 2-D-DPCA in this paper. The state space model of a 2-D batch process can be identified with canonical variate analysis (CVA) method based on the auto-determined support region (ROS). In 2-D-DPCA modeling, the utilization of state variables instead of lagged process variables reduces the number of variables and provides a clearer contribution plot for fault diagnosis.     

Bayesian inference and joint probability analysis for batch process monitoring

A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes, a phase‐based Bayesian inference strategy is introduced, which can efficiently combine the information of multiple operation modes together into a single model in each specific phase. Therefore, without any process knowledge, local monitoring results in different operation modes can be automatically integrated. Besides, the information of the operation mode can be obtained through joint probability analysis under the Bayesian monitoring framework. Potential extensions of the proposed method for fault diagnosis and identification are also discussed. A benchmark case study on the penicillin fermentation process is given to evaluate the feasibility and efficiency of the proposed method. It is demonstrated that the monitoring performance and the process comprehension have both been improved. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3702–3713, 2013     

Multivariate statistical process monitoring of batch‐to‐batch startups

In batch processes, multivariate statistical process monitoring (MSPM) plays an important role for ensuring process safety. However, despite many methods proposed, few of them can be applied to batch‐to‐batch startups. The reason is that, during the startup stage, process data are usually nonstationary and nonidentically distributed from batch to batch. In this article, the trajectory signal of each process variable is decomposed into a series of components corresponding to different frequencies, by adopting a nonparametric signal decomposition technique named ensemble empirical mode decomposition. Then, through instantaneous frequency calculation, these components can be divided into two groups. The first group reflects the long‐term trend between batches, which extracts the batch‐wise nonstationary drift information. The second group corresponds to the short‐term intrabatch variations. The variable trajectory signals reconstructed from the latter fulfills the requirements of conventional MSPM. The feasibility of the proposed method is illustrated using an injection molding process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3719–3727, 2015     

Optimization and nonlinear control of a batch crystallization process

Crystallization process has been widely used for separation in many chemical industries due to its capability to provide high purity product. To obtain the desired quality of crystal product, an optimal cooling control strategy is studied in the present work. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Two different optimization problems are formulated and solved by using a sequential optimization approach. Owing to the complex and nonlinear behavior of the batch crystallizer, the nonlinear control strategy which is based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile. The optimization integrated with nonlinear control strategy is demonstrated on a seeded batch crystallizer for the production of potassium sulfate.     

Modeling and monitoring of multimode process based on subspace separation

In the paper, a new process monitoring approach is proposed for handling the multimode monitoring problem in the industrial batch processes. Compared to conventional method, the contributions are as follows: a new method of extracting the common subspace of different modes is proposed based on the subspace separation; after that the two different subspaces are separated, the kernel principal component models is built for the common and specific subspace respectively and the monitoring is carried out in subspace. The monitoring is carried out in the subspaces. The corresponding confidence regions are constructed according to their models respectively.     

Enhanced high-order information extraction for multiphase batch process fault monitoring

Conventional independent component analysis (ICA) monitoring methods extract the feature information of process data by selecting more important independent components (ICs), which discard a small part of ICs that may contain useful information for faults, leading to unsatisfactory monitoring results. However, when the number of sampling points is greater than that of process variables, the ICA monitoring model does not work well. To address the aforementioned problems, a novel monitoring method, multiphase enhanced high-order information extraction (MEHOIE), is proposed in this paper. The entire production process was first divided into several steady phases and transition phases by the affinity propagation (AP) phase partitioning method. The enhanced high-order information extraction (EHOIE) model was then built in each phase for fault monitoring. Finally, the algorithm was applied in the penicillin simulation platform and industrial microbial pharmaceutical process. The flexibility and superiority of this algorithm were verified by comparing it with other conventional methods.     

Transfer of process monitoring models between plants: Batch systems

In this paper, the problem of transferring a process monitoring model between different batch plants is addressed. By exploiting the data coming from a source plant similar to the target plant to which a batch production needs to be transferred, it is possible to transfer a process monitoring model to the target plant under the assumption that the source plant and the target plant are driven by the same fundamental laws. Two transfer methodologies are proposed. The first one is based on multiway principal component analysis, and exploits the information of similar (“common”) variables that are measured both in the source plant and in the target plant. The second methodology is based on the use of multiway joint-Y partial least-squares regression and exploits all the measured data, i.e. common variables as well as variables that are measured only in one of the two plants. The effectiveness of the proposed methodologies is demonstrated using a benchmark simulated fed-batch fermentation process for penicillin production.     

Ewma charts for multivariate time series

In this paper a muIt,ivariat.e EWMA chart for time series is introduced. In principle, it is a generalization of the control scheme of Lowry et al. (I992) for multivarite indendent observations.

The autocovariances of the EWMA recursion are derived for stationary multivariate time series. IYsing tllese reslllts a co11t.rol chart hased or1 t11 illt.ivariate EWMA recursion is proposed. For a multivariate autoregressive process of order 1, a sufficient. condition is given such that the in-control average run length (ARL) is invariant, withrespect to the covariance of the White Noise process. This scheme is compared with the MEWMA control chart of Lowry et al. (1992) applied to the residuals.

By an extensive Monte carlo study the ARL of both charts are determined for several multivariate autoregressive processses.     

基于仿射传播聚类子集主元分析的间歇过程监测方法

针对间歇过程固有的多阶段特性,也为了克服传统阶段划分方法严格按照物理时刻顺序将采样点硬性分割而不能使其寻找数据特征最为相近的聚类中心的严重缺陷,提出基于仿射传播聚类(AP)的子集多向主元分析(subset-MPCA)监测新方法:采用全新的乱序聚类思想,将时间片矩阵打乱用AP进行无约束乱序聚类,使样本突破时间顺序的约束自由找寻与其特征最为相近的聚类中心,获得聚类子集,建立精确的子集MPCA监控模型。在线监控时,引入信息度传递实现实时采样点的阶段归属判断,解决阶段不等长批次的最佳模型选择问题。对青霉素仿真数据的实验表明,该方法较传统方法可有效降低故障的漏报和误报,有着更加可靠的监控性能。     

Applying wavelet-based hidden Markov tree to enhancing performance of process monitoring

In this paper, wavelet-based hidden Markov tree (HMT) models is proposed to enhance the conventional time-scale only statistical process model (SPC) for process monitoring. HMT in the wavelet domain cannot only analyze the measurements at multiple scales in time and frequency but also capture the statistical behavior of real world measurements in these different scales. The former can provide better noise reduction and less signal distortion than conventional filtering methods; the latter can extract the statistical characteristics of the unmeasured disturbances, like the clustering and persistence of the practical data which are not considered in SPC. Based on HMT, a univariate and a multivariate SPC are respectively developed. Initially, the SPC model is trained in the wavelet domain using the data obtained from the normal operation regions. The model parameters are trained by the expectation maximization algorithm. After extracting the past operating information, the proposed method, like the philosophy of the traditional SPC, can generate simple monitoring charts, easily tracking and monitoring the occurrence of observable upsets. The comparisons of the existing SPC methods that explain the advantages of the properties of the newly proposed method are shown. They indicate that the proposed method can lead to more accurate results. Data from the monitoring practice in the industrial problems are presented to help readers delve into the matter.     

Phase partition and KPI-related process monitoring for batch processes using a novel canonical correlation analysis strategy

Key performance indicators (KPI)-related process monitoring has been of great significance to ensure product quality and economic benefits for batch processes. Considering that different phases exhibit different characteristics, one of the key issues is how to partition the whole batch process into different phases and characterize them separately by multiple phase models. In order to model and monitor batch processes more accurately and efficiently, a novel canonical correlation analysis (CCA) strategy is proposed in this paper. The phase partition algorithm is designed based on the joint canonical variable matrix (JCVM). Different from previous methods, it considers the time sequence of operation phases and can distinguish the phase switches from dynamics anomalies. Using this algorithm, phases are separated in order from a KPI-related perspective, revealing high correlation among variables. After phase partition, a novel multi-phase local neighbourhood standardization CAA (MPLNSCCA) method focusing on KPI is set up for online monitoring, which could further address the misclassification problems. The advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli fermentation, respectively.     

大机组在线监测系统在大化肥装置上的应用

阐述在大化肥装置中，对大型压缩机组实行在线监测的重要性，以及大机组在线监测系统的应用情况．     

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

In the chemical industry, real‐time flooding prognosis is a necessity for packed‐column operation because the flooding phenomenon interferes with the performance of production systems. In this work, the profile monitoring technique is utilized to capture the dynamic behavior of pressure drop, which is an important indicator for the flooding phenomenon. In each moving window, the pressure drop signals are described by using an exponential generalized autoregressive conditional heteroskedastic model. The onset of the flooding phenomenon is then indicated by changes in model parameters. Moreover, to efficiently capture the process change, a nonparametric approach is utilized to establish a statistical control chart. Experimental and comparison results show the advantages of the proposed method.