Subspace identification for data‐driven modeling and quality control of batch processes

Corbett, Brandon; Mhaskar, Prashant

doi:10.1002/aic.15155

Cited by 100 publications

(91 citation statements)

References 57 publications

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“…[29][30][31] In addition, a series of dynamic models that do not suffer from this limitation have also been proposed recently. Corbett and Mhaskar [8] proposed a subspace identification-based modelling method to capture the dynamic evolution of a batch process and relate the subspace states to product quality. The method was further extended to account for more complicated mid-batch additions and address batch duration.…”

Section: Pls and Mpls Methodsmentioning

confidence: 99%

“…Partial least squares (PLS), principal component regression (PCR), artificial neural network (ANN), and subspace identification are effective data-driven quality prediction methods. [1][2][3][4][5][6][7][8][9] The methods mentioned above have attracted much attention for batch process modelling, monitoring, and quality prediction in recent years. The MPLS technique has become the most widely used multivariate statistical analysis technique for the quality prediction of batch processes.…”

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confidence: 99%

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Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

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Batch processes inherently have multiple operation phases; different phases exhibit different characteristics. Hence, it is reasonable to partition the process into phases and build sub‐phase models for online quality prediction. To this end, a sequential phase partition method based on the information increment is proposed. To address the multiphase behaviours in batch processes, this work utilizes a new information increment index to capture the dynamic characteristics of batch processes along a time direction and divides the process into sub‐phases. Next, phase‐based multiway partial least squares (MPLS) models are built to model within‐phase characteristics and predict the quality‐related variables online. Information increment is able to exploit the process evolution by focusing on the changing variable correlations derived from two adjacent extend time slice. It directly utilizes the available process measurements of successful history batch processes without data transformation or dimensionality reduction. The method is sequential and can overcome the limits of some phase partition methods that may divide the samples with discontinuous time sequence but similar characteristics into the same phase. In addition, the information increment is capable of reflecting the change of the process intuitively with high computation efficiency. Advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli (E. coli) fermentation, respectively.

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Section: Pls and Mpls Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…Each unfolding method has its own advantages, so the method to choose depends on the characteristics of the batch processes. The papers published in the last decade can be divided into three groups: (i) papers that applied the existing continuous process approaches to batch process monitoring to handle the non‐Gaussian problem, nonlinear problem, dynamic problem, and/or multimode problem; (ii) papers that focused on handling the uneven duration problem in batch processes; and (iii) papers that introduce industry applications of these algorithms …”

Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China.

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“…While there are a variety of computationally efficient linear [11,37,38] and nonnlinear [10,[39][40][41][42] MOR techniques available, in this work, we developed a reduced-order model (ROM) based on multivariate output error state space (MOESP) algorithm using the simulation results from the high-fidelity model as described in [8]. The developed ROM is presented as follows:…”

Section: Simulation Of Sub-optimal Control Policies For Adpmentioning

confidence: 99%

Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

2018

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Abstract:Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young's modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the "curse-of-dimensionality" associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young's modulus of the rock formation.

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Subspace identification for data‐driven modeling and quality control of batch processes

Cited by 100 publications

References 57 publications

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

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