Ph.D Defense: Joshua Daniel Brooks

Mon Nov 15 2021 09:00 AM
BlueJeans
A Framework for Selecting Multi-Attribute Optimal Renewable-Energy Driven Desalination Architectures

You're invited to attend

 

Ph.D. Defense

 

by

 

Joshua Daniel Brooks

(Advisor: Prof. Dimitri N. Mavris)

 

"A Framework for Selecting Multi-Attribute
Optimal Renewable-Energy Driven Desalination Architectures"

 

Monday, November 15
9:00 a.m.
BlueJeans:
https://bluejeans.com/347888678/2585

 

Abstract
The world is drifting into a near-future of unprecedented water resource management challenges. Growing water demand is projected to be met by limited, unpredictable, and in many locations shifting freshwater resources. Near-future populations are projected to face widespread water stress, most immediately and severely encountered in the form of hydrological drought. Desalination systems offer resilience in the form of additional water supplies which are insensitive to drought. However, desalination systems are currently limited by their costs, water inefficiency, greenhouse gas (GHG) emissions, energy requirements, and quality and environmental impacts, and are thus not used on the wider scale necessary to appropriately mitigate the risk of projected water stress.

This work aimed to help overcome desalination’s core barriers to adoption by introducing an original framework for the quantitative performance-based selection of multi-attribute optimal desalination architectures. This framework enables an expansive desalination architecture design space exploration across both desalting and energy subsystems. Desalination architectures were valuated by mapping their barriers to adoption to their quantifiable performance attributes: cost, GHG emissions, and freshwater recovery. A superstructure flowsheet model was constructed to include reverse osmosis, multi-stage flash, multi-effect distillation, and thermal vapor compression desalting technologies. This model was situated inside of an optimization routine and used to both explore an unprecedented desalting design space and to identify designs which often outperformed those identified in similar efforts.

An individual desalination architecture alternative in this work is defined as any desalting subsystem alternative connected to an optimal energy subsystem. An energy system model was therefore constructed to include photovoltaic arrays, wind energy converters, concentrated solar power plants, battery energy storage, and a connection to a conventional electrical grid and steam generator. Incorporating renewable energy sources (RES) enabled the identification of energy subsystems which lowered cost, GHG emissions, and water consumption compared to traditional grid and dedicated steam generation systems. High speed metamodels were successfully used to represent the full energy system model in order to make desalination architecture evaluation and optimization exercises computationally tenable.

The full desalination architecture evaluation environment, consisting of the integrated desalting and energy subsystem models, was situated within an optimization routine. Cost-driven optimization exercises consistently identified RES-driven desalination alternatives which outperformed conventional alternatives identified in similar efforts. In addition, multiple cases were demonstrated wherein the simultaneous consideration of both energy and desalting subsystem performance in desalination architecture optimization exercises identified alternatives which were uncompetitive using the traditional selection approach.

This thesis effort provides decision makers with a quantitative performance-based, tailorable framework for rapidly exploring the desalination architecture design space and selecting multi-attribute optimal systems regarding their unique preferences and system requirements. The constructed framework is flexible enough to accommodate different optimization and decision making techniques, and approaches are discussed for incorporating additional technologies into the desalting and energy subsystem modeling environments. This quantitative architecture selection framework, specifically its capability in allowing novel architectural and conceptual trades, is the core outcome of this work.

 

Committee:

  • Prof. Dimitri N. Mavris – School of Aerospace Engineering (advisor)
  • Prof. Devesh Ranjan– School of Aerospace Engineering
  • Dr. Scott Duncan – School of Aerospace Engineering
  • Dr. Richard Simmons – School of Mechanical Engineering
  • Dr. Akanksha Menon – School of Mechanical Engineering

Location

BlueJeans