Accurate and timely fault diagnosis is a vital task to ensure process safety of modern
industrial facilities. Motivated by the complex variations of process signals and mutual …

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

Advances in technology allowed the fast and easy creation and configuration of industrial
alarms. The growth in the number of alarms, however, brought some problems to the alarm …

Reconstruction of process networks from data through the detection of cause–effect
relationships between process variables is of great importance in the analysis of …

Data-driven soft sensors, aiming to estimate and predict hard-to-measure quality variables
using easy-to-measure process variables, have now become the key foundation for …

Co-processing biogenic feedstocks allows oil refiners to use their infrastructure while
reducing the carbon intensity of the fuels they produce. Although policies such as British …

In discrete manufacturing systems, the efficiency of quality problem solving always matters,
but it can decrease continually due to increasing systematic complexity, uncertainty, and …

Decarbonization of the oil refining industry is essential for reducing carbon emissions and
mitigating climate change. Co-processing bio feed at existing oil refineries is a promising …

In process industries, quality variables such as concentrations and viscosity usually require
offline laboratory analysis due to difficulties associated with online sensing and are often …

Due to the high-stakes nature of industrial processes, there is an immediate and pressing
need on soft sensors for stability and interpretability. In this regard, causality-inspired …