Process Data Analytics in the Era of Big Data

Qin, S. Joe

doi:10.1002/aic.14524

Cited by 42 publications

(52 citation statements)

References 37 publications

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“…The constants 2 , 4 , 6 … are empirically chosen to obtain sufficient resolution, while preventing significant negative dips in the baseline or a significant decrease in S/N. Generally, the 6 th -order and higher derivatives were not required, and the following conditions were proposed as starting points for the selection of the values: 2 = σ 2 30 and 4 = σ 4 200 . This approach was then demonstrated on various overlapping peaks in LC analysis, namely single critical pairs, such as isomers of salbutamol and p-nitro-DL-phenylalanine, and difficult-to-separate mixtures of three differently deuterated benzenes.…”

Section: Derivative Enhancementmentioning

confidence: 99%

“…New analytical tools typically are able to generate more and more complex data, from which it is increasingly difficult to extract useful information and deduce simple and correct answers, especially when multi-component samples are analyzed. To extract all valuable information from what has been referred to by some as "a tsunami of data" or, more generally, "Big Data," efficient data-analysis strategies are evidently needed [2].…”

Section: Introductionmentioning

confidence: 99%

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Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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Section: Derivative Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

View full text Add to dashboard Cite

show abstract

“…For global process (the second level) (1). When the local process has not been affected, • is out of control, it means that this disturbance (operating condition change or process fault) just affects local process behaviors.…”

Section: Monitoring Policymentioning

confidence: 99%

Distributed Dynamic Modeling and Monitoring for Large-Scale Industrial Processes under Closed-Loop Control

Zhao

Huang

2018

Ind. Eng. Chem. Res.

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For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between real process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop industrial processes by concurrently exploring static and dynamic characteristics.First, the large-scale closed-loop process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and real industrial process data to illustrate the effectiveness of the proposed method.

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“…3 Big data is reshaping both operations research and process systems engineering. [30][31][32] More recently, dramatic progress of mathematical programming methods, 33 coupled with recent advances in machine-learning algorithms, 34 sparks a flurry of interest in the research field of data-driven optimization. [35][36][37][38][39] For example, a data-driven framework to design uncertainty sets for static robust optimization was proposed based on statistical hypothesis tests.…”

Section: Introductionmentioning

confidence: 99%

“…42 In addition, process data are often contaminated with anomalous measurements. 30 Anomalous measurements in process industries arise due to various factors, such as significant sensor noise, disturbance in processes, or human errors. They could undesirably affect the estimated region of uncertain parameters, and therefore would have an impact on uncertainty set.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

2017

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A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data‐driven approach for defining uncertainty set is proposed. This machine‐learning model is seamlessly integrated with adaptive robust optimization approach through a novel four‐level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multilevel optimization problem cannot be solved directly by any off‐the‐shelf solvers, an efficient column‐and‐constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3790–3817, 2017

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Process Data Analytics in the Era of Big Data

Cited by 42 publications

References 37 publications

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Distributed Dynamic Modeling and Monitoring for Large-Scale Industrial Processes under Closed-Loop Control

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

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