A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems
N Md Nor, CR Che Hassan… - Reviews in Chemical …, 2020 - degruyter.com
Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …
variations and detect abnormal changes in a process plant. It is always important for early …
Reactive crystallization: a review
MA McDonald, H Salami, PR Harris… - Reaction Chemistry & …, 2021 - pubs.rsc.org
Reactive crystallization is not new, but there has been recent growth in its use as a means of
improving performance and sustainability of industrial processes. This review examines …
improving performance and sustainability of industrial processes. This review examines …
Review of recent research on data-based process monitoring
Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
Process monitoring using variational autoencoder for high-dimensional nonlinear processes
In many industries, statistical process monitoring techniques play a key role in improving
processes through variation reduction and defect prevention. Modern large-scale industrial …
processes through variation reduction and defect prevention. Modern large-scale industrial …
Soft‐sensor development using correlation‐based just‐in‐time modeling
K Fujiwara, M Kano, S Hasebe, A Takinami - AIChE Journal, 2009 - Wiley Online Library
Soft‐sensors have been widely used for estimating product quality or other key variables,
but their estimation performance deteriorate when the process characteristics change. To …
but their estimation performance deteriorate when the process characteristics change. To …
Process monitoring based on independent component analysis− principal component analysis (ICA− PCA) and similarity factors
Many of the current multivariate statistical process monitoring techniques (such as principal
component analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian …
component analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian …
A comparative study of just-in-time-learning based methods for online soft sensor modeling
Most traditional soft sensors are built offline and only to be used online. In modern industrial
processes, the operation condition is changed frequently. For these time-varying processes …
processes, the operation condition is changed frequently. For these time-varying processes …
Fault detection for nonlinear process with deterministic disturbances: A just-in-time learning based data driven method
Data-driven fault detection plays an important role in industrial systems due to its
applicability in case of unknown physical models. In fault detection, disturbances must be …
applicability in case of unknown physical models. In fault detection, disturbances must be …
Improved kernel PCA-based monitoring approach for nonlinear processes
Conventional kernel principal component analysis (KPCA) may not function well for
nonlinear processes, since the Gaussian assumption of the method may be violated through …
nonlinear processes, since the Gaussian assumption of the method may be violated through …
Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
HK Mohanta, AK Pani - Applied Soft Computing, 2022 - Elsevier
Real time estimation of target quality variables using soft sensor relevant to time varying
process conditions will be a significant step forward in effective implementation of Industry …
process conditions will be a significant step forward in effective implementation of Industry …