Abstract
This article presents a hybrid methodology for the intelligent control of the reactor-regenerator unit of a residual fluid catalytic cracking (RFCC) plant under conditions of uncertain input information. The main challenge in catalytic cracking process control is the instability of feedstock composition and the delay in laboratory data on product quality, which complicates the operational management of the RFCC process while maintaining the required gasoline quality. The objective of this study is to support effective management of the gasoline production process by maximizing the yield of high-quality gasoline while keeping its density within specified limits. The proposed architecture combines a regression model built on six dominant parameters selected through Pearson’s correlation analysis, a Mamdani-type fuzzy inference system with a database of 26 expert rules, and a machine-learning module (Random Forest and gradient boosting) that corrects the residual error of the combined regression-fuzzy model, allowing the control system to adapt to changing process conditions. Testing the developed models on real data from the Shymkent Oil Refinery shows that the hybrid model reduces the root-mean-square error from 0.1973 (regression model alone) to 0.0161 and the mean absolute percentage error from 0.328% to 0.028%, while the coefficient of determination increases from 0.9895 to 0.9999; this improved accuracy is estimated to correspond to an approximate 3.2% increase in achievable gasoline yield and a 0.4% improvement in density stability. The developed decision support system for catalytic cracking process control, based on the proposed methodology, acts as a “virtual analyzer”, ensuring effective control in real time without the use of expensive in-line quality control devices.