The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and
SEMMA has grown substantially over the past decade. However, little is known as to how …

More than two decades have passed since the first holistic data-driven approaches for batch
data analysis (BDA) were published. The emphasis was on multivariate statistical process …

Abstract In the Industry 4.0 era, the chemical industry is embracing broad adoption of
artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic …

The quality of information generated in data‐driven empirical studies is of central importance
in Industry 4.0. However, despite the undeniable and widely accepted importance, not …

Statistical process monitoring (SPM) is an important component in the long-term reliable
operation of any system and its importance can only become greater in the era of smart …

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing
significant losses in efficiency, controllability, and plant economy, as well as an increased …

In semiconductor manufacturing, virtual metrology (VM), also known as soft sensor, is the
prediction of wafer properties using process variables and other information available for the …

Conventional data-driven models for batch processes conduct unfolding operations and
neglect the continuous property. A novel soft sensor method is proposed based on the …

The number and diversity of Process Analytics applications is growing fast, impacting areas
ranging from process operations to strategic planning or supply chain management …

A novel data driven approach to batch process monitoring is presented, which combines the
k-Nearest Neighbour rule with the dynamic time warping (DTW) distance. This online …