Name: Sebastian Antonio Romo Duenas

Defense Date: June 21st, 2022 at 1pm.

Location: Glenn Martin Hall, EGR-2162.

Committee Members:

Jelena Srebric, Ph.D. (Advisor/Chair)

Reinhard Radermacher, Ph.D.

Bao Yang, Ph.D.

Dongxia Liu, Ph.D.

Peter Sunderland, Ph.D. (Dean’s Representative)

 A Validated Modeling Framework for Performance Analyses of Experimental and Proven Desalination Technologies


There is a wide array of desalination methods available for treating water at different salinities and production rates, but there are no systemic approaches on how to directly compare performance of different desalination systems. Existing comparison efforts focus solely on isolated performance metrics for a single desalination system, resulting in segregated case studies and/or incomparable systems. Numerical models for desalination systems can bridge this gap as they can take account of specific deployment needs. However, models in the literature are not mutually compatible, and they seldom disclose all the parameters or equations necessary for development and validation. This dissertation conceives a cross-comparison enabling simulation framework for the most relevant desalination processes. To achieve this, modeling approaches and thermophysical property correlations are curated from volumes of literature and used to create metamodels for six relevant desalination methods. The models are integrated into a simulation framework based on parameter hierarchies imposed in the model structures. The simulation suite is validated with data from the literature and actual operational data from desalination facilities in the field. 

The results show that the cross-comparison across equal parameter hierarchies is possible for all desalination technologies. A comparative analysis between the dominant technologies in the thermal and molecular transport families, Multi-Effect Distillation (MED) and Reverse Osmosis (RO), respectively, shows that energy intensity in MED is an order of magnitude greater for equivalent operational conditions, but actual operational costs are comparable. The models are further refined to reflect conditions from actual systems in the field and an iterative sampling algorithm is developed to find plausible operation scenarios given the scarce data from the field. This method achieves excellent agreement with data from four desalination plants with percent differences ranging between 2.5% and 9.3%. Furthermore, the results identify two plants performing 20% below their theoretically achievable recovery. Apart from evaluating existing deployments, the simulation suite helps identify a niche in the operational map of existing desalination methods characterized by high recovery rates and high feed salinities that is generally unfulfilled by conventional desalination.