Hamayesh Arwin Alborz همایش آروین البرز ETSJR فصلنامه نوآوری در مهندسی و فناوری | ETSJR 2 2 2026 09 17 Predicting the Performance of Fluid Catalytic Cracking (FCC) or Hydrocracking Catalysts Using Deep Learning Models Predicting the Performance of Fluid Catalytic Cracking (FCC) or Hydrocracking Catalysts Using Deep Learning Models 294 310 FA Mahsa Nadimpiur 1Department of Chemistry, Faculty of Basic Sciences, Shahid Chamran University, Ahvaz, Iran Mahsa Nadimpiur 1Department of Chemistry, Faculty of Basic Sciences, Shahid Chamran University, Ahvaz, Iran 2026 09 05 Fluid Catalytic Cracking (FCC) units are critical yet operationally complex systems characterized by severe non-linearities, strong phase interactions, and harsh thermodynamic environments, making real-time monitoring and product yield prediction challenging. While traditional first-principles kinetic and hydrodynamic models provide mechanistic fidelity, their high computational cost limits real-time closed-loop control. Conversely, purely data-driven soft sensors often suffer from poor generalization and lack interpretability. This study presents a comprehensive state-of-the-art review and comparative framework integrating multi-lump kinetic models, multiphase hydrodynamic intensification, and advanced deep learning architectures—including spatial-temporal neural networks, physics-guided neural networks (PGNN), and explainable artificial intelligence (XAI). By synthesizing mechanistic multi-phase interactions in the reactor-regenerator loop with data-driven soft sensing methodologies, we outline critical design trade-offs in prediction accuracy, physical consistency, and computational overhead. Furthermore, feature attribution frameworks utilizing Shapley Additive Explanations (SHAP) are evaluated to ensure transparent, trustworthy decision-making for plant-wide optimization. This work establishes a robust blueprint for developing next-generation, physics-informed digital twins and intelligent soft sensors in sustainable petroleum refining. Fluid Catalytic Cracking (FCC) units are critical yet operationally complex systems characterized by severe non-linearities, strong phase interactions, and harsh thermodynamic environments, making real-time monitoring and product yield prediction challenging. While traditional first-principles kinetic and hydrodynamic models provide mechanistic fidelity, their high computational cost limits real-time closed-loop control. Conversely, purely data-driven soft sensors often suffer from poor generalization and lack interpretability. This study presents a comprehensive state-of-the-art review and comparative framework integrating multi-lump kinetic models, multiphase hydrodynamic intensification, and advanced deep learning architectures—including spatial-temporal neural networks, physics-guided neural networks (PGNN), and explainable artificial intelligence (XAI). By synthesizing mechanistic multi-phase interactions in the reactor-regenerator loop with data-driven soft sensing methodologies, we outline critical design trade-offs in prediction accuracy, physical consistency, and computational overhead. Furthermore, feature attribution frameworks utilizing Shapley Additive Explanations (SHAP) are evaluated to ensure transparent, trustworthy decision-making for plant-wide optimization. This work establishes a robust blueprint for developing next-generation, physics-informed digital twins and intelligent soft sensors in sustainable petroleum refining. https://www.etsjr.ir/downloadfilepdf/17913