Optimization of multistage olefin/paraffin membrane separation processes through rigorous modeling

Abstract

In this work, we explore the capabilities of an NLP optimization model to determine the viability of facilitated transport membrane processes intended to replace traditional distillation currently employed for propane/propylene separation. An NLP optimization model for multistage membrane processes has been formulated, introducing the mathematical description of the facilitated transport mechanisms in the PVDF-HFP/BMImBF 4 /AgBF 4 membranes previously developed by our research group. For the first time, a simultaneous optimization of the process and the membrane material (i.e., carrier concentration) has been performed, thanks to the implementation of the governing equations for the fixed site and mobile carrier mechanisms. Once the model is solved in GAMS it returns the optimal membrane area, carrier loading and permeate pressure of each stage based on Net Present Value Cost (NPVC) minimization. Different process flow sheets were evaluated and the results show prominent reductions on NPVC for facilitated transport multistage processes when compared to distillation.

Membrane Separation Process Design and Intensification

“…Hasan et al employed nonlinear programming (NLP) for the optimal design of multistage membrane processes to separate CO 2 from multicomponent flue gas mixtures. Zarca et al also used NLP to optimize multistage olefin–paraffin membrane separation processes. Aliaga-Vicente et al and Ohs et al employed mixed integer nonlinear programming (MINLP) to optimize membrane cascade for gas separation.…”

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

“…Thus, process optimization using this approach is currently not possible. On the other hand, heuristics or short-cut models can be embedded computational resource-efficiently in complex optimization superstructures [36][37][38][39][40][41][42]. However, these simplified heuristics or short-cut models do not sufficiently describe the influence of membrane parameters, such as the synthesis protocols or process environment, on the membrane performance.…”

Evaluation of experimentally obtained permeance based on module simulation: How should permeance be evaluated?

“…In a cross‐flow model, the permeated component flows directly to the outlet without mixing, while in cocurrent and countercurrent models, the downstream is mixed along the membrane. Membrane module performances such as flow rate and concentration in the permeate and retentate have been frequently simulated using these models in an effort to obtain optimal operating conditions 1‐9 . This type of module simulation requires given membrane permeation properties that include permeance, P i , and permeance ratio, α p , in order to calculate the module performance.…”

“…Membrane module performances such as flow rate and concentration in the permeate and retentate have been frequently simulated using these models in an effort to obtain optimal operating conditions. [1][2][3][4][5][6][7][8][9] This type of module simulation requires given membrane permeation properties that include permeance, P i , and permeance ratio, α p , in order to calculate the module performance. On the other hand, from the viewpoint of an experiment, membrane permeation properties (P i and α p ) are obtained from experimental data using a module.…”