A novel stochastic approach was developed to estimate the construction and operation cost of a seawater reverse osmosis (SWRO) desalination plant. Uncertainties in the future cost of energy (e.g., electricity price) and financing (e.g., interest and inflation rates) were represented by probability distributions, and correlations among these variable parameters (i.e., statistical dependence between two or more variables) were estimated based on past records. The stochastic cost model was further coupled with a process simulation model that predicts performance measures such as water production rate and produced water quality. The coupled model was applied to a case study in which a virtual full-scale SWRO plant is constructed using two configuration options: single-stage single-pass (SSSP) and double-stage single-pass (DSSP). The case study demonstrates the effectiveness of the coupled model in ranking and comparing a large number of design and operating conditions for the full-scale SWRO plant. The main criteria for comparison were (1) the ability to meet the required production rate and water quality standards, such as salt and boron rejection rates, and (2) the unit cost of water production. The results show that cost estimates and relative rankings of various design and operating conditions from the stochastic approach are often different than those determined by the deterministic approach. More importantly, the stochastic model allows the decision maker to quantitatively consider the uncertainties in the cost estimates for this energy-intensive technology.
Bibliographical noteFunding Information:
This research was supported by the National Science Foundation under Grant Number CMMI-0644837 and the Seawater Engineering & Architecture of High Efficiency Reverse Osmosis (SeaHero) project by Korean Ministry of Land, Transport and Maritime Affairs. It was also partially supported by GS Engineering & Construction Inc.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Physical and Theoretical Chemistry
- Filtration and Separation