Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

As the most effective indicator for energy saving and emission reduction, energy efficiency
evaluation is widely used in complex petrochemical industries. It is nowadays common to …

The CO 2-to-light olefins technology represents a significant approach to mitigating the
greenhouse effect and advancing green energy solutions. However, little literature …

Just-in-time Learning (JITL) is a soft measurement method commonly used in industrial
processes, which can update local models in real-time to solve the problem of inaccurate …

Extraction of underlying patterns from measured variables is central to various data-driven
control applications, such as soft-sensor modeling, statistical process monitoring, and fault …

Gasoline is the main fuel for small vehicles, and the exhaust emissions from its combustion
have a major impact on the atmospheric environment. In the cumbersome process of …

Soft sensors estimate quality indicators that are difficult to measure online so that they are
important in industrial processes. The sensors may malfunction so that some data may be …

Supervised representation learning based on the teacher-student framework can extract
quality-related features for soft sensors, in which the teacher network extracts representation …

Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the
prominent ability of exploring dynamic information of the industrial process, SFA could …

Gasoline is one of the major products of oil and petrochemical industry. Blending is the final
step and key to improve the efficiency of gasoline production. As an important property that …