Fluid catalytic cracking (FCC) is one of the most important processes in the renewable energy as well as petrochemical industries. The prediction and understanding of the FCC performance in a real industrial environment is still challenging, as this is a highly complex process affected by many extremely non-linear and interrelated factors. In this paper, a novel hybrid predictive framework for FCC is developed by integrating a data-driven deep neural network with a physically meaningful lumped kinetic model, powered by orders of magnitude greater number of high-quality data from a modem automated FCC process. The results show that the novel hybrid model exhibits best predictions with regards to all the evaluation criteria such as Mean Absolute Percentage Error, Pearson coefficient, and standard deviation. It indicates that the hybrid data-driven deep learning with mechanistic kinetics model creates a better approach for fast prediction and optimization of complex reaction processes such as FCC.

流化催化裂化（FCC）是可再生能源以及石化工业中最重要的过程之一。在实际的工业环境中，FCC性能的预测和理解仍然具有挑战性，因为这是一个非常复杂的过程，受许多极端非线性和相互关联的因素的影响。在本文中，通过将数据驱动的深度神经网络与具有物理意义的集总动力学模型相集成，开发了一种新颖的FCC混合预测框架，该模型由来自调制解调器自动化FCC流程的大量高质量数据提供支持。结果表明，对于所有评估标准，例如平均绝对百分比误差，皮尔逊系数和标准偏差，该新型混合模型均表现出最佳的预测。