Multivariate statistical process control methods for batch production: a review focused on applications

Ramos, Miriam; Ascencio, José Salazar; Hinojosa, Miriam Vanessa; Vera, Francisco; Ruiz, O.; Jimenez-Feijoó, María Isabel; Galindo-Villardón, Purificación

doi:10.1080/21693277.2020.1871441

Cited by 7 publications

(3 citation statements)

References 62 publications

(72 reference statements)

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“…These approaches are grounded in the idea that patterns and behaviors observed in the past can be used to establish a baseline for normal system operation [8]. In data-based approaches, Statistical approaches, such as Multivariate Statistical Process Control (MSPC) and control charts [9][10][11], and machine learning methods, such as neural networks, support vector machines, and decision trees, are employed for fault detection by learning patterns and relationships from data [12,13]. Conventional methods for Multivariate Statistical Process Monitoring, designed to monitor multivariate processes, comprise Principal Component Analysis (PCA) [14,15], Independent Component Analysis (ICA) [16,17], and Partial Least Squares (PLS) [18,19].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Kini,

Madakyaru,

Harrou

et al. 2024

ChemEngineering

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Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

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Section: Introductionmentioning

confidence: 99%

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Kini,

Madakyaru,

Harrou

et al. 2024

ChemEngineering

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“…In recent years, multivariate statistical process monitoring (MSPM) and machine learning have become more standard approaches. These methods aid in the modeling of large scale batch processes that are subject to disturbances that were not foreseen or could not be modeled based on mechanistic or semiempirical models. − In recent reviews, Ramos et al discusses some methods commonly used in applications dealing with batch processes, while Ebadi et al focus on specific methods that target the covariance matrix of the process. Despite the diversity of existing and novel methods, the black box nature of these modeling strategies results in one common limitation, i.e., their limited interpretability.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of the Spray Drying Process. Process Monitoring and Prediction of the Particle Size in Pharmaceutical Production

Muñoz López,

Peeters,

Van Impe

2024

ACS Omega

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Spray drying is used in the pharmaceutical industry for particle engineering of amorphous solid dispersions (ASDs). The particle size of the spray-dried (SD) powders is one of their key attributes due to its impact on the downstream processes and the drug product's functional properties. Offline and inline laser diffraction methods can be used to estimate the product's particle size; however, the final release of these ASDs is based on offline instruments. This paper presents a novel data-driven modeling approach for predicting the particle size of SD products. The modelbased characterization of the process and the product's particle size, as a critical quality attribute, follows the quality by design principles. The resulting model can be used for online process monitoring, reducing the risks of out-of-specifications products and supporting their real-time release. A Tucker3 model is trained to capture and factorize the deterministic variability of the process. Subsequently, a partial least-squares regression model is calibrated to model the impact that variability in the input material properties, the process parameters, and the spray nozzle have on the products' particle size. This strategy has been calibrated and validated using large scale production data for two intermediate drug products under high sparsity of particle size data. Despite the challenges, high accuracy was obtained in predicting the median particle size (dv50) for release. The 99% confidence interval results in an error of maximum 2.5 μm, which is less than 10% of the allowed range of variation.

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“…[15]- [17] proposed adaptive versions of MCUSUM and MEWMA charts for the process mean based on fixed and variable sampling intervals. We refer interested to [18]- [22] for some recent enhancements of the MEWMA, MCUSUM and MHWMA charts.…”

Section: Introductionmentioning

confidence: 99%

One-Sided and Two One-Sided Multivariate Homogeneously Weighted Moving Charts for Monitoring Process Mean

Adegoke

Riaz

Ganiyu³

2021

IEEE Access

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Multivariate memory-type control charts that use information from both the current and previous process observations have been proposed. They are designed to detect shifts in both upper and downward directions with equal precision when monitoring the process mean vector. The absence of directional sensitivity can limit the charts' application, particularly when users are interested in detecting variations in one direction than the other. This article proposes one-sided and two one-sided multivariate control charts for monitoring shifts in the process mean vector. The proposed charts are presented in the form of the multivariate homogeneously weighted moving average approach that yields efficient detection of shifts in the mean vector. We provide simulation studies under different shift sizes in the process mean vector and evaluate the performance of the proposed charts in terms of their run length properties. We compare the average run length (ARL) results of the charts with the conventional charts as well as the onesided and two one-sided multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) charts. Our simulation results show that the proposed charts outperform the existing charts used for the same purpose, particularly when interest lies in detecting small shifts in the mean vector. We show how the charts can be designed to be robust to non-normal distributions and give a step-by-step implementation efficient application of the charts when their parameters are unknown and need to be estimated. Finally, an illustrative example is provided to show the application of the proposed charts.INDEX TERMS Average run length; multivariate homogeneously weighted moving average; one-sided control charts; two one-sided control charts, robustness, estimation.

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Multivariate statistical process control methods for batch production: a review focused on applications

Cited by 7 publications

References 62 publications

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Data-Driven Modeling of the Spray Drying Process. Process Monitoring and Prediction of the Particle Size in Pharmaceutical Production

One-Sided and Two One-Sided Multivariate Homogeneously Weighted Moving Charts for Monitoring Process Mean

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