Optimization of reverse osmosis networks for seawater desalination

Voros, N.G.; Maroulis, Z.B.; Marinos-Kouris, D.

doi:10.1016/0098-1354(96)00068-3

Cited by 44 publications

(27 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most process-optimization schemes specify the membranes and then determine the conditions for optimal performance [14][15][16][17][18][19][20][21][22][23][24][25]. In contrast, the analysis of the EERO process does not specify the membranes, but determines the membrane rejections required in each stage for optimal performance.…”

Section: Membrane Area Requiredmentioning

confidence: 93%

“…(12) and for Y 3 = 0.5 and 0.7 for the 4-stage EERO process predicted by Eq. (16). Owing to the symmetry of Eq.…”

Section: Required Membrane Areamentioning

confidence: 94%

“…2; the recycle ratio R 1 required to maintain a desired χ 1 can be determined from Eq. (16). Retentate recycle might also be necessary in stage 3 to maintain a sufficiently high χ 3 .…”

Section: Opd and Net Sec For 4-stage Eero Processmentioning

confidence: 97%

See 2 more Smart Citations

Energy-efficient reverse osmosis desalination: Effect of retentate recycle and pump and energy recovery device efficiencies

et al. 2015

View full text Add to dashboard Cite

Section: Membrane Area Requiredmentioning

confidence: 93%

“…(12) and for Y 3 = 0.5 and 0.7 for the 4-stage EERO process predicted by Eq. (16). Owing to the symmetry of Eq.…”

Section: Required Membrane Areamentioning

confidence: 94%

See 1 more Smart Citation

Energy-efficient reverse osmosis desalination: Effect of retentate recycle and pump and energy recovery device efficiencies

et al. 2015

View full text Add to dashboard Cite

“…El-Halwagi [16] first formulated the reverse osmosis network (RON) optimization problem as a mixed integer nonlinear programming (MINLP) problem and proposed a resolution method for the cost minimization of the hollow fiber membranes RON in waste reduction applications. Voros and Fazilet [17,18] simplified El-Halwagi's representation by using a variable split ratio to formulate the problem as a nonlinear programming (NLP) problem. Marcovecchio et al [19] proposed an iterative resolution method for the optimization of the total annual cost of either one-stage or two-stage hollow fiber PV configurations.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of RO desalination plants

et al. 2008

View full text Add to dashboard Cite

A process optimization method has been developed for the design of reverse osmosis (RO) processes. RO process configurations are systematically generated using a flexible superstructure and evaluated by economical (investment and operating costs), technical (energy requirement, water recovery rate) and environmental performance indicators (Life Cycle Assessment). The simultaneous optimization of the RO process layout and operating conditions constitutes a mixed-integer nonlinear programming (MINLP) problem, which is solved using a multi-objective optimization (MOO) approach. The MOO identifies the best technological alternatives for the set of selected objectives. In a given context, it allows to define a set of optimal solutions representing the trade-off between conflicting objectives such as economical costs and environmental impacts. As a case study, the methodology is applied on a brackish water reverse osmosis (BWRO) desalination project, for which the optimal design is characterized depending on the economical conditions.

show abstract

“…Papafotiou et al (1992) developed an expert system for the synthesis of a reverse-osmosis desalination plant. Voros et al (1996) used a superstructure in order to identify the optimum reverse osmosis network structure and predict the performance of the units employed in the plant.…”

Section: Introductionmentioning

confidence: 99%