Real-Time Optimization and Estimation for CO2 Purification Process

Biogas upgrading generates CO 2 as a byproduct, which presents a valuable opportunity for reuse, particularly in the food and beverage industry where high-purity CO 2 is required. This study addresses the development of a real-time optimization framework that integrates Kalman filter estimation techniques for controlling a CO 2 scrubber. A dynamic simulation model is developed to represent the process, and two optimization algorithms, including gradient-based methods and Genetic Algorithms (ga), are utilized to identify optimal solvent flow setpoints. The conventional control strategy (L/G control) employs the total gas mass flow and a fixed liquid-to-gas ratio, which is systematically compared to the outcomes from the real-time optimizer. Findings indicate that the developed strategy significantly enhances overall plant performance compared to the standard control strategy, highlighting the benefit of advanced estimation and optimization methods in CO 2 purification processes.