Abstract The Fluid Catalytic Cracking unit (FCC) is a key process that plays an important
technical and economical role in the refining industry. Over the past years, there has been …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Catalytic cracking is one of the most important refining processes used in the petrochemical
industry, in which the highly active zeolite catalyst is the key to heavy oil catalytic cracking …

Data-driven models require high-fidelity data of sufficient quantity and granularity. This is
challenging in a complex chemical processing system due to frequent sensor breakdown …

Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …

Fluid catalytic cracking (FCC) is a key unit in the petrochemical production process with
frequently encountered abnormal conditions and great safety challenge due to its complex …

The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals
requires the development of a robust model that represents the crude-to-chemical …

Catalytic chemical processes such as hydrocracking, gasification and pyrolysis play a vital
role in the renewable energy and net zero transition. Due to the complex and non-linear …

In a chemical process, abnormal conditions may lead to process fluctuations or unplanned
shutdowns, resulting in serious economic losses and even safety accidents. Early prediction …

Data-driven method has been widely used in Fluid Catalytic Cracking (FCC) process
modeling. However, due to the complexity of chemical process both in time and spatial …