A review on the modernization of pharmaceutical development and manufacturing–Trends, perspectives, and the role of mathematical modeling
Recently, the pharmaceutical industry has been facing several challenges associated to the
use of outdated development and manufacturing technologies. The return on investment on …
use of outdated development and manufacturing technologies. The return on investment on …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence
C Zhao - Journal of Process Control, 2022 - Elsevier
The development of the Internet of Things, cloud computing, and artificial intelligence has
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
Performance supervised plant-wide process monitoring in industry 4.0: A roadmap
The intensive research and development efforts directed towards large-scale complex
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes
Modern industrial plants generally consist of multiple manufacturing units, and the local
correlation within each unit can be used to effectively alleviate the effect of spurious …
correlation within each unit can be used to effectively alleviate the effect of spurious …
A review on data-driven process monitoring methods: Characterization and mining of industrial data
Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
A review of kernel methods for feature extraction in nonlinear process monitoring
Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …
patterns in any data set. In this paper, the applications of kernel methods for feature …
A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear …
H Zhang, C Zhang, J Dong, K Peng - International Journal of …, 2022 - Taylor & Francis
The industrial process monitoring and operating performance assessment techniques are of
great significance to ensure the safety and efficiency of the production and to improve the …
great significance to ensure the safety and efficiency of the production and to improve the …
Local–global modeling and distributed computing framework for nonlinear plant-wide process monitoring with industrial big data
Industrial big data and complex process nonlinearity have introduced new challenges in
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
Robust and sparse canonical correlation analysis for fault detection and diagnosis using training data with outliers
A well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack
of robustness against outliers. This shortcoming hinders the application of CCA in the case …
of robustness against outliers. This shortcoming hinders the application of CCA in the case …