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You searched for: Journal Industrial & engineering chemistry process design and development Remove constraint Journal: Industrial & engineering chemistry process design and development Subject process monitoring Remove constraint Subject: process monitoring Subject case studies Remove constraint Subject: case studies Text Availability Citation in PubAg Remove constraint Text Availability: Citation in PubAg
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1. Adaptive Selection of Latent Variables for Process Monitoring

Author:
Lijia Luo; Shiyi Bao; Jianfeng Mao
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.21 pp. 9075-9086
ISSN:
1520-5045
Subject:
case studies; chemistry; process monitoring
Abstract:
... Latent-variable (LV) methods have been widely applied to multivariate statistical process monitoring. Using LV methods can simplify the process-monitoring problem through a projection of the high-dimensional process data into a low-dimensional LV space that retains most of the important information for fault detection. A key issue is the optimal selection of a subset of LVs to constitute such a lo ...
DOI:
10.1021/acs.iecr.8b05847

2. Concurrent Monitoring Strategy for Static and Dynamic Deviations Based on Selective Ensemble Learning Using Slow Feature Analysis

Author:
Huifen Hong; Chao Jiang; Xin Peng; Weimin Zhong
Source:
Industrial & engineering chemistry process design and development 2020 v.59 no.10 pp. 4620-4635
ISSN:
1520-5045
Subject:
Bayesian theory; algorithms; case studies; models; principal component analysis; process monitoring
Abstract:
... Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the prominent ability of exploring dynamic information of the industrial process, SFA could monitor the process static and dynamic deviations concurrently. However, for complex and large-scale processes, it is difficult for a single SFA model to monitor the whole process well because of the complex relationship ...
DOI:
10.1021/acs.iecr.9b05547

3. A Debottlenecking Study of an Industrial Pharmaceutical Batch Plant

Author:
Franz D. Bähner; Jakob K. Huusom
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.43 pp. 20003-20013
ISSN:
1520-5045
Subject:
algorithms; business enterprises; case studies; equipment; information processing; models; process design; process monitoring; systems engineering
Abstract:
... Operational complexity in batch-operated plants is large, and companies struggle to produce at high equipment utilization. Owing to high fixed costs, capacity utilization is an important operational directive. Many current plants are not in a state that allows the application of rigorous process systems engineering tools due to modeling challenges. This work proposes identifying the right engineer ...
DOI:
10.1021/acs.iecr.9b03134

4. Fault Detection and Root Cause Analysis of a Batch Process via Novel Nonlinear Dissimilarity and Comparative Granger Causality Analysis

Author:
He Fei; Wang Chaojun; Fan Shu-Kai S
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.47 pp. 21842-21854
ISSN:
1520-5045
Subject:
algorithms; batch systems; case studies; data collection; entropy; fermentation; penicillins; process monitoring
Abstract:
... Data-driven fault detection and root cause analysis methods become attractive in modern industrial production that can guarantee the safety and stability of process operation. If process monitoring technology is implemented for fault detection, and the root cause of faults is analyzed timely, it is beneficial to maintain and improve the quality of coming batches. In this paper, a framework of faul ...
DOI:
10.1021/acs.iecr.9b04471

5. Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics

Author:
Chunhui Zhao; Biao Huang
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.14 pp. 5045-5057
ISSN:
1520-5045
Subject:
algorithms; case studies; covariance; process design; process monitoring; variance
Abstract:
... For a nonstationary process which has a time-varying mean, a time-varying variance, or both, it can be difficult to detect incipient disturbances which may be hidden by the time-varying process variations. Besides, stationary and nonstationary characteristics may coexist in complex industrial processes which, however, have not been studied for process monitoring. In the present work, a triple subs ...
DOI:
10.1021/acs.iecr.8b00233

6. Industrial Process Monitoring Based on Knowledge–Data Integrated Sparse Model and Two-Level Deviation Magnitude Plots

Author:
Lijia Luo; Shiyi Bao; Jianfeng Mao; Zhenyu Ding
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.2 pp. 611-622
ISSN:
1520-5045
Subject:
case studies; models; normal values; process design; process monitoring
Abstract:
... Data-driven process monitoring methods use only process data to build monitoring models, while useful process knowledge is completely ignored. Consequently, data-driven monitoring models usually have poor interpretability, which may reduce fault detection and diagnosis capabilities. To overcome such drawbacks, a novel knowledge–data integrated sparse monitoring (KDISM) model and two-level deviatio ...
DOI:
10.1021/acs.iecr.7b02150

7. Joint Structure Preserving Embedding Model and Its Application for Process Monitoring

Author:
Yuanjian Fu; Chaomin Luo
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.45 pp. 20667-20679
ISSN:
1520-5045
Subject:
case studies; geometry; learning; models; principal geodesic analysis; process design; process monitoring
Abstract:
... Currently, dimensionality reduction methods based on manifold learning are widely applied to industrial process monitoring. However, considering that a number of manifold-based monitoring methods extract only the global geometric information of the original data or the local manifold structure of the neighboring data points, in this article, a novel dimensionality reduction method called joint str ...
DOI:
10.1021/acs.iecr.9b03077

8. New Dynamic Predictive Monitoring Schemes Based on Dynamic Latent Variable Models

Author:
Yining Dong; S. Joe Qin
Source:
Industrial & engineering chemistry process design and development 2020 v.59 no.6 pp. 2353-2365
ISSN:
1520-5045
Subject:
case studies; data collection; models; prediction; process monitoring; uncertainty
Abstract:
... In this paper, dynamic predictive monitoring schemes based on dynamic latent variable models are proposed. We consider the most typical case in industrial data where dynamics usually exist in a reduced dimensional subspace. First, using dynamic latent variable models, predictions are made to reduce the dynamic latent space variability and focus on the unpredictable variabilities for process monito ...
DOI:
10.1021/acs.iecr.9b04741

9. Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis

Author:
Qun-Xiong Zhu; Yi Luo; Yan-Lin He
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.45 pp. 20655-20666
ISSN:
1520-5045
Subject:
alarm systems; case studies; least squares; models; process design; process monitoring
Abstract:
... Process monitoring and alarm systems play an important role in production safety. Distributed alarm systems have been utilized to effectively reduce the burden of data calculations, contributing to fast-track root causes of alarms. However, the traditional distributed contribution analysis method cannot be well applied to online monitoring in time. In addition, the correlation-based variable recon ...
DOI:
10.1021/acs.iecr.9b02963

10. Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

Author:
Yuan Xu; Cuihuan Fan; Qun-Xiong Zhu; Abbas Rajabifard; Nengcheng Chen; Yiqun Chen; Yan-Lin He
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.4 pp. 1619-1630
ISSN:
1520-5045
Subject:
case studies; models; multivariate analysis; process monitoring; seeds
Abstract:
... With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological f ...
DOI:
10.1021/acs.iecr.9b05403

11. Optimal Feature Selection for Distributed Data-Driven Process Monitoring

Author:
Shaaz Khatib; Prodromos Daoutidis
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.6 pp. 2307-2317
ISSN:
1520-5045
Subject:
algorithms; case studies; computers; mathematical theory; process monitoring
Abstract:
... Two methods are proposed in this article to address distributed data-driven process monitoring. The first method selects sensors and allocates these sensors among subsystems with the objective of optimizing the diagnostic performance of a pattern recognition method when it is implemented in a distributed configuration subject to constraints that are imposed to meet operational requirements. The se ...
DOI:
10.1021/acs.iecr.9b03872

12. Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

Author:
Jingxin Zhang; Maoyin Chen; Hao Chen; Xia Hong; Donghua Zhou
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.14 pp. 5579-5587
ISSN:
1520-5045
Subject:
algorithms; case studies; process design; process monitoring; statistical analysis
Abstract:
... By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP–MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, t ...
DOI:
10.1021/acs.iecr.8b05875

13. Reconstruction-Based Multivariate Process Fault Isolation Using Bayesian Lasso

Author:
Zhengbing Yan; Yuan Yao; Tsai-Bang Huang; Yi-Sern Wong
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.30 pp. 9779-9787
ISSN:
1520-5045
Subject:
case studies; industry; probability; process design; process monitoring; product quality; regression analysis
Abstract:
... To ensure process safety and product quality, multivariate statistical process monitoring (MSPM) has been widely used in industry for decades. Among the steps of MSPM, fault isolation is an important link between the fault detection and the root-cause diagnosis, which identifies the variables closely related to the detected process abnormality. The existing methods for fault isolation often suffer ...
DOI:
10.1021/acs.iecr.7b05189

14. Recursive Gaussian Mixture Models for Adaptive Process Monitoring

Author:
Junhua Zheng; Qiaojun Wen; Zhihuan Song
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.16 pp. 6551-6561
ISSN:
1520-5045
Subject:
Bayesian theory; batch systems; case studies; models; probability; process design; process monitoring; variance covariance matrix
Abstract:
... Gaussian mixture models (GMM) have recently been introduced and widely used for process monitoring. This paper intends to develop a new recursive GMM model for adaptive monitoring of processes under time-varying conditions. Two model updating schemes with/without forgetting factors are both proposed. Bayesian inference probability index is used as the monitoring statistic in both of the continuous ...
DOI:
10.1021/acs.iecr.8b06101

15. Robust Monitoring of Industrial Processes in the Presence of Outliers in Training Data

Author:
Shiyi Bao; Lijia Luo; Jianfeng Mao; Di Tang; Zhenyu Ding
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.24 pp. 8230-8239
ISSN:
1520-5045
Subject:
case studies; covariance; models; multivariate analysis; process design; process monitoring
Abstract:
... Data-driven process-monitoring methods often use classical multivariate statistical analysis (MSA) techniques for data analysis and modeling, with the assumption that the training data contains only observations associated with normal operating conditions of a process. In practice, the training data of an industrial process may contain outliers. Classical MSA methods can be affected by outliers so ...
DOI:
10.1021/acs.iecr.8b00464

16. Robust Self-Supervised Model and Its Application for Fault Detection

Author:
Li Jiang; Zhihuan Song; Zhiqiang Ge; Junghui Chen
Source:
Industrial & engineering chemistry process design and development 2017 v.56 no.26 pp. 7503-7515
ISSN:
1520-5045
Subject:
case studies; neural networks; principal component analysis; process design; process monitoring
Abstract:
... Previous work on process monitoring has shown that chemical processes can be modeled by data-based models such as principal component analysis (PCA) and neural network models. However, it is difficult to train a model that has good generalization capabilities in fault detection, especially for nonlinear processes. On the basis of the idea of making the trained model robust with respect to the nois ...
DOI:
10.1021/acs.iecr.7b00949

17. Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

Author:
Lijia Luo; Shiyi Bao; Chudong Tong
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.3 pp. 1300-1309
ISSN:
1520-5045
Subject:
case studies; models; principal component analysis; process design; process monitoring; variance covariance matrix
Abstract:
... Principal component analysis (PCA) is a popular method for modeling and analysis of high-dimensional data. In spite of its advantages, classical PCA also has two drawbacks. First, it is very sensitive to outliers in the data. Second, it cannot yield interpretable PCs because most of the loadings are nonzero. To overcome these drawbacks, we propose a new PCA method that has the properties of robust ...
DOI:
10.1021/acs.iecr.8b04655

18. System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering

Author:
Shaaz Khatib; Prodromos Daoutidis; Ali Almansoori
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.24 pp. 8283-8298
ISSN:
1520-5045
Subject:
case studies; principal component analysis; process design; process monitoring; system optimization
Abstract:
... Conventional multivariate statistical process monitoring methods such as Principal Component Analysis perform poorly in detecting faults in large systems. Partitioning the system and implementing a multivariate statistical process monitoring method in a distributed manner improves monitoring performance. A simulation optimization method is proposed whose objective is to find the system decompositi ...
DOI:
10.1021/acs.iecr.8b01708

19. A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II—Assessing Detection Speed

Author:
Tiago J. Rato; Ricardo Rendall; Veronique Gomes; Pedro M. Saraiva; Marco S. Reis
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.15 pp. 5338-5350
ISSN:
1520-5045
Subject:
batch systems; case studies; probability; process design; process monitoring; retrospective studies
Abstract:
... Since the first batch process monitoring approaches were published in the literature approximately 20 years ago, a significant number of extensions and new contributions have been proposed. They enrich the toolkit of solutions made available to practitioners, who face today the daunting task of finding the best tool among all of them, as well as defining and tuning all associated configuration opt ...
DOI:
10.1021/acs.iecr.7b04911

20. Systematic Procedure for Granger-Causality-Based Root Cause Diagnosis of Chemical Process Faults

Author:
Han-Sheng Chen; Zhengbing Yan; Yuan Yao; Tsai-Bang Huang; Yi-Sern Wong
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.29 pp. 9500-9512
ISSN:
1520-5045
Subject:
case studies; industrial applications; process design; process monitoring; statistical analysis; time series analysis
Abstract:
... Multivariate statistical process monitoring (MSPM) has received a considerable amount of attention in terms of both academic research and industrial applications. Most of these efforts have been focused on fault detection and isolation, while root cause diagnosis has not yet been fully addressed. In recent years, data-driven causality analysis methods have been adopted in order to understand the c ...
DOI:
10.1021/acs.iecr.8b00697

21. A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes

Author:
Ricardo Rendall; Bo Lu; Ivan Castillo; Swee-Teng Chin; Leo H. Chiang; Marco S. Reis
Source:
Industrial & engineering chemistry process design and development 2017 v.56 no.30 pp. 8590-8605
ISSN:
1520-5045
Subject:
analytical methods; batch systems; case studies; data analysis; dryers; prediction; process design; process monitoring
Abstract:
... We present a data analytics framework for offline analysis of batch processes. The framework provides a unified setting for implementing several variants of feature oriented analysis proposed in the literature, including a new methodology based on the process variables’ profiles presented in this article. It also integrates feature generation and feature analysis, in order to speed up the data exp ...
DOI:
10.1021/acs.iecr.6b04553

22. A Variable-Correlation-Based Sparse Modeling Method for Industrial Process Monitoring

Author:
Lijia Luo; Shiyi Bao; Zhenyu Ding; Jianfeng Mao
Source:
Industrial & engineering chemistry process design and development 2017 v.56 no.24 pp. 6981-6992
ISSN:
1520-5045
Subject:
case studies; models; process design; process monitoring
Abstract:
... Dimensionality reduction techniques are widely used in data-driven process monitoring methods for extracting key process features from process data. Dimensionality reduction generally leads to information loss, and therefore may reduce the process monitoring performance. However, choosing an appropriate data projection matrix that can minimize the effect of dimensionality reduction on process moni ...
DOI:
10.1021/acs.iecr.7b00057