The Woodruff School

SUMMARY

Water desalination is a reliable process for supplying freshwater water, but geography, cost (operating/capital), and environmental impacts pose significant challenges for widespread adoption. Today, nearly all (~ 99\%) desalination plants rely on fossil fuels as the primary energy source to produce heat or electricity to drive the desalination process. If this trend continues, carbon emissions from fossil fuel-powered desalination plants could increase to 400 million tons of CO2 per year by 2050. Additionally, the projected waste brine produced at current desalination plants by 2050 may approach 240 km3 per year. Solar desalination technologies (thermal and electric) could provide a sustainable path toward achieving high volume (million gallons per day) renewable driven desalted water while achieving minimal or zero liquid discharge (MLD and ZLD). This is of growing interest in an effort to minimize waste (carbon and brine). Yet, efficient integration between solar capture and desalination remains as critical challenge. Furthermore, the high-energy intensity required to reach saturation in MLD/ZLD processes is a critical obstacle. The aim of this PhD dissertation is to propose a framework that integrates thermodynamics, geographical information systems, and machine learning for developing and predicting sustainable strategies to integrate solar energy with desalination, in an effort to contribute to the development of a sustainable industry with reduced CO2 emissions and brine rejection. Using computational models for large-scale systems and data analysis, this research program aims to evaluate the current state of desalination worldwide and the potential of solar thermal hybrid desalination systems for producing freshwater with low brine rejection. The use of geographic information systems benefits the analysis process allowing to integrate geospatial data. The use of machine learning benefits the processing of high amounts of data and system optimization, decreasing the computational time and resource consumption.

SUBJECT:	Ph.D. Dissertation Defense

BY:	Rodrigo Caceres Gonzalez

TIME:	Wednesday, June 29, 2022, 1:00 p.m.

PLACE:	Love Building, 295

TITLE:	Development and Prediction of Sustainable Strategies for Integrating Solar Energy With Desalination

COMMITTEE:	Dr. Marta C. Hatzell, Chair (ME) Dr. Sheldon Jeter (ME) Dr. Peter Loutzenhiser (ME) Dr. Emily Grubert (CEE) Dr. Andrey Gunawan (Intel Corporation) Dr. Akanksha Menon (ME) Dr. George "Bud" Peterson (ME)

SUMMARY Water desalination is a reliable process for supplying freshwater water, but geography, cost (operating/capital), and environmental impacts pose significant challenges for widespread adoption. Today, nearly all (~ 99\%) desalination plants rely on fossil fuels as the primary energy source to produce heat or electricity to drive the desalination process. If this trend continues, carbon emissions from fossil fuel-powered desalination plants could increase to 400 million tons of CO2 per year by 2050. Additionally, the projected waste brine produced at current desalination plants by 2050 may approach 240 km3 per year. Solar desalination technologies (thermal and electric) could provide a sustainable path toward achieving high volume (million gallons per day) renewable driven desalted water while achieving minimal or zero liquid discharge (MLD and ZLD). This is of growing interest in an effort to minimize waste (carbon and brine). Yet, efficient integration between solar capture and desalination remains as critical challenge. Furthermore, the high-energy intensity required to reach saturation in MLD/ZLD processes is a critical obstacle. The aim of this PhD dissertation is to propose a framework that integrates thermodynamics, geographical information systems, and machine learning for developing and predicting sustainable strategies to integrate solar energy with desalination, in an effort to contribute to the development of a sustainable industry with reduced CO2 emissions and brine rejection. Using computational models for large-scale systems and data analysis, this research program aims to evaluate the current state of desalination worldwide and the potential of solar thermal hybrid desalination systems for producing freshwater with low brine rejection. The use of geographic information systems benefits the analysis process allowing to integrate geospatial data. The use of machine learning benefits the processing of high amounts of data and system optimization, decreasing the computational time and resource consumption.