Abstract
Pressure Swing Adsorption (PSA) is widely used process for gas separation. Recently, some researchers have been trying to use this PSA process for upgrading bio-gas. The bio-gas mainly consists of methane and carbon dioxide. Highly purified methane gas can be used for energy production, when the methane gas is separated from the carbon dioxide. However, during bio-gas extraction the composition and flow rate are slowly changing. Due to these changes, undesirable product gas properties of will be obtained. The efficiency of PSA process including recovery, purity and productivity are affected by operating conditions, such as feed pressure, feed velocity, p/f ratio, step-time etc. The aim of this research is dynamic optimization of PSA operation for bio-gas upgrading process considering feed composition variations. The objective is maximization of methane recovery, at the purity constraints, while control variables are step times for each step and Purge/Feed (P/F) ratio at regeneration step.
In this research, robust PSA model is developed for dynamic simulation and optimization using gPROMS™ to solve problem. For improving accuracy of the model, distribution method is used; Central Finite Difference Method (CFDM), 2 level in this model. Especially, the time variables are treated as control variables in this model. Due to the discrete changes of boundary and equations, the solving of this optimization problem needs high skills and strategies. The ‘SRQPD’ solver, one of the NLP solvers, has been used, applying new equations with binary variables which can describe which time belongs to which step.
