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We present a multi-objective analysis of the optimization of seawater reverse osmosis (RO) desalination systems, considering economic, power requirement, and exergo-economic factors. Exergy flow for each stream and exergy destruction for each equipment in the RO network is included in the model. The exergo-economic unit cost (EUC) for the final product is considered as an objective function. An enhanced version of the augmented epsilon constraint method, AUGMECON2, is utilized to solve the multi-objective optimization problem, which allows for lexicographic optimization for other objectives if alternative optimum solutions exist. Redundant iterations are avoided through the use of the bypass coefficient. Results show that the Pareto solutions for permeate split design are located below the normal design. Fixed cost dominates the exergo-economic unit cost. The power cost has the greatest share of water cost, while pass 1 has the greatest share of exergy destruction. The throttling valves and blending of different salinity streams (final product mixer and first pressurization stage mixer) could cause non-negligible exergy destruction. The fixed cost for each equipment, exergy rate, and EUC for each flow rate are analyzed, in order to provide insights into the system.

Multi-effect distillation-thermal vapor compression (MED–TVC) systems have attracted increasing attention due to their relatively low steam consumption. In this work, comprehensive thermal and exergy analyses of MED and MED–TVC systems were implemented. Besides, a detailed energy utilization diagram (EUD) analysis was conducted to reveal the mechanism of the internal exergy loss. According to the EUD analysis, TVC could solve energy level mismatch and improve energy utilization. However, the system's exergy loss was greatly increased. With the introduction of TVC, the performance ratio (Pr) increased from 10.88 to 14.11, and the energy consumption (Q) decreased from 224.09 kJ/kg-Mp to 181.30 kJ/kg-Mp. In addition, a countercurrent grouping feed was adopted in the system. The influence of the suction position combined with different effect group division methods for the 13-effect MED–TVC system was also investigated. Five different effect group division methods were analyzed in terms of system efficiency, performance ratio, exergy destruction, and energy consumption. The results indicated that a more uniform distribution of effects among the effect groups was beneficial for improving the performance of the MED–TVC system. Furthermore, both the Pr and system exergy efficiency (y) improved with an increase in the suction effect number.

This study proposes an optimal study of reverse osmosis (RO) seawater desalination system based on the split partial second pass (SPSP) design in order to fully utilize better quality and higher flux for the permeate at the front of the pressure vessel (PV). Differential equations in the membrane transport model are solved by finite difference method, which considers the longitudinal variation of the superficial velocity, the pressure and the salinity inside the PV. Superstructure optimization is adopted to determine optimal flow structure, appropriate membrane type, and operation conditions. Firstly, single product RO system has been optimized, cost breakdown and sensitive analysis reveal that membrane with large active area and high salt rejection is favored for SPSP. System recovery, pressure, and split ratio for the permeate of pass 1 should be adjusted with time in order to decrease total annualized cost. Secondly, multiple product RO system has been studied. Three-pass RO configuration with permeate re-processed and brine recycled is favored for the system with two permeate products. The optimal RO configuration with three permeate products is influenced by flow rates and required permeate concentrations. In general, SPSP could provide lower cost, lower energy consumption, and smaller system size than normal design.

This study proposes a multi-objective optimization (MOO) of reverse osmosis (RO) networks for seawater desalination. The membrane transport model takes into consideration of the longitudinal variation of the velocity, the pressure, and the concentration in the membrane modules. The RO network with three type energy recovery device options (pressure exchanger (PX), Hydraulic Turbocharger, and turbine) is introduced. Lexicographic optimization (for calculation of a more effective payoff table) and augmented e-constraint method (to avoid inefficient Pareto solutions) are proposed to solve the MOO problem. A fuzzy decision maker is introduced to derive the most efficient solution among Pareto-optimal solutions. Firstly, different energy recovery option studies show that using PX is seen to be the most profitable option. Exergy analysis is used to evaluate the contribution of the equipments in energy degradation. Secondly, the proposed multi-objective framework simultaneously optimizes the total annualized cost (TAC) and energy consumption. With the increases of weighting for the main objective function: TAC, the most efficient solution moves to lower TAC direction. Finally, system recovery rate is added as the third objective function. It is reasonable to stay at the appropriate system recovery rather than to increase up to its limit and generating high energetic losses.