Adaptive Modeling Strategy Integrating Feature Selection and Random Forest for Fluid Catalytic Cracking Processes

This study proposes a hybrid approach for the modeling of the fluid catalytic cracking (FCC) process, with the aim to establish an adaptive and accurate product yield prediction model. Because of the uncertainties in crude oil quality and the complexity of the FCC process, which, for example, has highly coupled process variables with high dimensionality and strong interference, it is difficult for existing first-principles-based methodologies to deliver accurate results. To tackle this, this study proposes a machine-learning-based modeling approach that integrates an intelligent feature selection strategy with random forest for the process modeling. First, the adaptive immune genetic algorithm (ALGA) is applied to screen for the most relevant process indicators from the collected process data, including the operation parameters for the relevant process devices and the property data of the feed stream. Second, random forest (RF) is employed to establish the FCC process models based on the selected process indicators in the first step. The approach is illustrated by its application in a real FCC production process, for which 10 months of historical production data were used to train and test the proposed AIGA-RF model to determine the product yield predictions for four products. Comparisons between the proposed method and other methods were also conducted. The result indicates that the proposed method is able to remove the disturbance variables and is found to be adaptable to different product yield prediction scenarios. It could be a good reference for online process optimization and control of FCC processes.