Maximize the operating profit of a SWRO-PRO integrated process for optimal water production and energy recovery
KAUST DepartmentWater Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/621799
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AbstractPressure retarded osmosis (PRO) is a promising technology to reduce the specific energy consumption and the operating expenditure of a seawater reverse osmosis (SWRO) plant. In this study, a simple analytical PRO model is developed to predict the PRO performance as the dilution of draw solutions occurs. The model can predict the PRO performance with a high accuracy without carrying out complicated integrations and experiments. The operating profit of SWRO-PRO is also studied by calculating the profit generated for every m3 of seawater entering the process because maximizing the operating profit is the uttermost objective of the SWRO-PRO process. Based on the PRO analytical model, the operating profit and the dynamics of the SWRO-PRO process, a strategy has been proposed to maximize the operating profit of the SWRO-PRO process while maintaining the highest power density of the PRO membranes. This study proves that integration of SWRO with PRO can (1) push the SWRO to a higher recovery and maintain its high profitability, (2) effectively reduce the specific energy consumption of desalination by up to 35% and (3) increase the operating profit up to 100%. © 2016 Elsevier Ltd.
CitationWan CF, Chung T-S (2016) Maximize the operating profit of a SWRO-PRO integrated process for optimal water production and energy recovery. Renewable Energy 94: 304–313. Available: http://dx.doi.org/10.1016/j.renene.2016.03.057.
SponsorsThis research is supported by the Singapore National Research Foundation, Prime Minister's Office, Singapore, under its Environmental & Water Technologies Strategic Research Programme, administered by the Environment & Water Industry Programme Office (EWI) of the PUB under the project titled "Membrane Development for Osmotic Power Generation, Part 2. Module Fabrication and System Integration" (1102-IRIS-11-01) and NUS grant no. R-279-000-382-279. The special thanks are due to due to Dr. Gang Han, Dr. Sui Zhang, Dr. Xue Li, Dr. Junying Xiong and Mr. Zhenlei Cheng for their kind help.