Simulation-Based Optimization of a Multiple Gas Feed Sweetening Process.

Area of Science:

• Chemical Engineering
• Process Systems Engineering

Background:

• Industrial sweetening processes often combine sour gases from multiple sources into a single plant, leading to energy intensity.
• Previous work introduced a multiple gas feed sweetening process to enhance hydrogen sulfide (H2S) purity and decrease energy usage.

Purpose of the Study:

• To develop a superstructure for the multiple gas feed sweetening process.
• To implement a simulation-based optimization framework for process enhancement.

Main Methods:

• Utilized Aspen HYSYS as the process simulator and particle swarm optimization (PSO) algorithm as the optimizer.
• Employed stepwise convergence adjustment for challenging distillation columns to ensure model convergence.
• Leveraged the simulator's built-in algorithm robustness for improved model convergence and reduced optimization time.

Main Results:

• Successfully developed and optimized the superstructure for the multiple gas feed sweetening process.
• Demonstrated improved H2S purity and reduced energy consumption compared to conventional methods.
• Validated the robustness and effectiveness of the simulation-based optimization approach through a case study.

Conclusions:

• The developed simulation-based optimization framework is effective for enhancing sour gas sweetening processes.
• The methodology offers guidance for model selection, process simplification, and optimization in similar absorption processes.
• This approach contributes to more efficient and sustainable industrial gas sweetening operations.