In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and
correlated data has paved the way for DL-based fault detection and diagnosis (FDD) …

Accurate and real-time estimation of iron ore sintering quality index is essential for the
stability of the production process. However, the sintering process data is generally …

Big data and time series are typical features of modern industrial process data. Effective time
series modeling methods are required for ensuring the normal and stable operation of …

This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

Deep learning models such as the long short-term memory (LSTM) network have been
applied for dynamic inferential modeling. However, many studies apply LSTM as a black …

Process dynamic behaviors resulting from closed-loop control and the inherence of
processes are ubiquitous in industrial processes and bring a considerable challenge for …

Data-driven soft sensors have been widely applied to a broad range of process industries for
virtually sensing difficult-to-measure but of-great-concern variables. However, it is still …

Causal discovery approaches are gaining popularity in industrial processes. Existing causal
discovery algorithms can indeed find some important causal relationships from industrial …