Modular Surrogate Models for Simulating the Amine Scrubbing Process

Surrogate models are becoming an important technique in process design, control, and optimization. These models are often developed at the process level, tailored to specific process configurations. Even when unit-level models are constructed, they rarely account for all units within a flowsheet. In this work, we use a unit-based surrogate model framework for an amine scrubbing process designed to remove CO 2 , H 2 S, and other sulfur species from gas streams. Unit-level Artificial Neural Network (ANN) surrogate models are created for each process unit. The developed unit-based models remain independent of process configuration and can be connected to recreate many possible arrangements of the amine scrubbing process. To demonstrate the versatility of our approach, we present two case studies. The first involves a typical amine scrubbing process configuration, while the second considers two absorber columns operating in parallel. This unit-based surrogate approach proves scalable and modular, enabling accurate predictions across diverse process configurations. By adopting this unit-based surrogate modeling framework, we can explore various process scenarios and simulate the amine scrubbing operations across multiple plant configurations.