Abstract
This paper fits into the process system engineering field by addressing the optimization of a two-stage membrane system for H2 separation in refinery processes. To this end, a nonlinear mathematical programming (NLP) model is developed to simultaneously optimize the size of each membrane stage (membrane area, heat transfer area, and installed power for compressors and vacuum pumps) and operating conditions (flow rates, pressures, temperatures, and compositions) to achieve desired target levels of H2 product purity and H2 recovery at a minimum total annual cost. Optimal configuration and process design are obtained from a model which embeds different operating modes and process configurations. For instance, the following candidate ways to create the driving force across the membrane are embedded: (a) compression of both feed and/or permeate streams, or (b) vacuum application in permeate streams, or (c) a combination of (a) and (b). In addition, the potential selection of an expansion turbine to recover energy from the retentate stream (energy recovery system) is also embedded. For a H2 product purity of 0.90 and H2 recovery of 90%, a minimum total annual cost of 1.764 M$·year−1 was obtained for treating 100 kmol·h−1 with 0.18, 0.16, 0.62, and 0.04 mole fraction of H2, CO, N2, CO2, respectively. The optimal solution selected a combination of compression and vacuum to create the driving force and removed the expansion turbine. Afterwards, this optimal solution was compared in terms of costs, process-unit sizes, and operating conditions to the following two sub-optimal solutions: (i) no vacuum in permeate stream is applied, and (ii) the expansion turbine is included into the process. The comparison showed that the latter (ii) has the highest total annual cost (TAC) value, which is around 7% higher than the former (i) and 24% higher than the found optimal solution. Finally, a sensitivity analysis to investigate the influence of the desired H2 product purity and H2 recovery is presented. Opposite cost-based trade-offs between total membrane area and total electric power were observed with the variations of these two model parameters. This paper contributes a valuable decision-support tool in the process system engineering field for designing, simulating, and optimizing membrane-based systems for H2 separation in a particular industrial case; and the presented optimization results provide useful guidelines to assist in selecting the optimal configuration and operating mode.
Highlights
Membranes are playing an important role in a wide range of industrial applications [1].If compared with other separation processes, membrane-based separation processes require simple pieces of equipment which are easy to operate and imply low investment and operating costs [2]
Regarding the optimal distribution of the total utility cost CRM, it can be seen in Figure 4c that the cost for electric power demand CEP is the largest contributor to CRM with 90.8%, followed by the cost for membrane replacement CMR with 7.3%
This paper presented the optimization results of two-stage membrane processes for H2 separation from a multi-component gas mixture by applying nonlinear mathematical programming approach for cost minimization
Summary
Membranes are playing an important role in a wide range of industrial applications [1]. When designing cost-efficient multi-stage membrane systems, which are required to simultaneously achieve high product purity and high recovery levels [16,17,18], all trade-offs among the model variables must be investigated. Qi and Henson [26] employed their model to study several case studies such as natural gas sweetening and enhanced oil recovery, considering multi-component separations and using DICOPT (DIscrete and Continuous OPTimizer) as optimization solver They highlighted the robustness of the proposed MINLP model. Zarca et al [30] concluded that a two-stage membrane process can be successfully applied for H2 recovery by using a polymeric membrane, and for syngas recovery by using a polymer-ionic liquid composite membrane Their conclusion is supported by a techno-economic analysis based on a nonlinear mathematical programming (NLP) model developed for this purpose, which was implemented and solved in GAMS/CONOPT environment. Of freedom of the resulting optimization problem and, the trade-offs exiting between the model variables, as it is clearly explained devoted to the process description
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