Reverse electrodialysis: Modelling and performance analysis based on multi-objective optimization. (15th May 2018)

• Record Type: Journal Article
• Authors: Long, Rui; Li, Baode; Liu, Zhichun; Liu, Wei
• Is Part Of: Energy. Volume 151(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-05-15
• Subjects: Reverse electrodialysis (RED) -- Modeling -- Multi-objective optimization -- Power density -- Energy efficiency; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.03.003
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this paper, we proposed a refined model to describe the RED process by considering the variation of flow rates along the flow direction, and the concentration depended density and viscosity. The model was verified by good accordance between the calculated and experimental measured data. For evaluating the performance of a RED stack for some special applications, the net power density and the energy efficiency are two main criteria. However, they could not achieve their maximum values simultaneously. To achieve such a compromise, an optimization based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) was conducted. Besides, the net power output and energy efficiency under single-objective optimization methods were calculated and compared. Results revealed that compared to the results under the maximum net power density, the net power density under the multi-objective optimization is slightly less than the maximum one, meanwhile the energy efficiency was much greater. The performance under the multi-objective optimization exhibited no obvious disadvantage against that under the maximum energy efficiency, considering the significant increase of the net power density. Highlights: A refined model considering flow rate changes and concentration depended properties is presented. An optimal analysis based on multi-objective optimization is conducted. Net power density under multi-objective optimization is slightly less than the maximum one. Energy efficiency is much larger than that under the single-objective optimization for net power density.