Adaptive Fuzzy Control of Petroleum Extraction Columns Using Quantum-Inspired Optimization

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. Unlike classical fuzzy controllers that rely solely on expert-defined rules, our approach utilizes a quantum-inspired optimization algorithm to adaptively refine fuzzy rule weights based on performance feedback. While the physical quantum effects at macroscopic industrial scales are negligible, the proposed method emulates the computational advantages of quantum systems, such as parallel rule evaluation and probabilistic amplitude processing, in a classical environment. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted under controlled conditions using synthetic data and varying operational parameters to measure improvements in control performance. The hybrid controller achieved a 0.7 % reduction in phenol consumption and reduced temperature deviations by 2.2 % compared to a baseline fuzzy controller. Energy savings ranged from 1 % to 2 % depending on operating scenarios. These results were supported by repeated simulations and statistical analyses across multiple testing cycles. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications.