(105h) Optimizing the Food-Energy-Water Nexus – from Surrogate Modeling to Informed Decision-Making

A growing global population, depleting water resources, increasing energy demands, together with climate change are immense stressors of food, energy, and water supply systems [1]. The food-energy-water nexus (FEWN) postulates that sustainable decision-making regarding the interconnected resources food, energy and water must consider all involved resources holistically, since e.g. energy system solution strategies influence the food and water system and vice versa [2]. Process systems engineering (PSE) has the capability to model real-world processes and systems to help guide decision makers by employing a systematic and integrated analysis of process structures. However, modeling challenges regarding the FEWN due to its multi-spatial and multi-temporal scales remain [3]. To overcome these challenges, we propose employing surrogate models based on data-driven and model optimization techniques. Therefore, this work presents an interconnected food-energy-water resource decision-making model based on surrogates to inform decision makers.

A comprehensive and complex mathematical model of the food supply system can be reformulated based on available food production data, e.g. for a pilot scale greenhouse, as resource constraints influencing the energy and water supply models [4]. To illustrate challenges within the energy supply system a framework for designing renewable energy systems under resource considerations is utilized, incorporating the intermittency and seasonality of renewable energies. Furthermore, complex power output vs. cost relationships have to be captured, while regional land and water constraints have to be taken into account. In this case, we employ clustering technique and preprocessing optimization models to derive cost function surrogates and reduce the overall model complexity [5]. Generating purified water for several applications can be achieved by reverse osmosis (RO) desalination plans. The modeling of RO plants is computationally expensive due to the complex mass transfer behavior of the membrane models. Therefore, artificial neural networks can be employed to approximate the plant behavior, ultimately enabling RO plant optimization [6]. Overall, this results in a mixed-integer linear optimization model incorporating not only food, energy, and water resources, but also regional dependencies of generated solution strategies. Moreover, the derived framework can readily be extended to multi-objective optimization by not only minimizing cost but also maximizing resource utilization and therefore guide informed decision making especially for semi-arid and arid resource scarce regions.

References

[1] Marcello Di Martino, Styliani Avraamidou, Julie Cook, Efstratios N. Pistikopoulos. An Optimization Framework for the Design of Reverse Osmosis Desalination Plants under Food-Energy-Water Nexus Considerations, Desalination, 503, 2021. https://doi.org/10.1016/j.desal.2021.114937

[2] R. Cory Allen, Yaling Nie, Styliani Avraamidou, Efstratios N. Pistikopoulos. Infrastructure Planning and Operational Scheduling for Power Generating Systems: An Energy-Water Nexus Approach, Computer Aided Chemical Engineering, 47, 233-238, 2019. https://doi.org/10.1016/B978-0-12-818597-1.50037-0

[3] Daniel J. Garcia, Fengqi You. The water-energy-food nexus and process systems engineering: A new focus, Computers & Chemical Engineering, 91, 2016, Pages 49-67. https://doi.org/10.1016/j.compchemeng.2016.03.003

[4] Sarah Namany, Tareq Al-Ansari, Rajesh Govindan. Sustainable energy, water and food nexus systems: A focused review of decision-making tools for efficient resource management and governance, Journal of Cleaner Production, 225, 2019, Pages 610-626. https://doi.org/10.1016/j.jclepro.2019.03.304

[5] Julie Cook, Marcello Di Martino, R. Cory Allen, Efstratios N. Pistikopoulos, Styliani Avraamidou. A decision-making framework for the optimal design of renewable energy systems under energy-water-land nexus considerations, Science of The Total Environment, 827, 2022. https://doi.org/10.1016/j.scitotenv.2022.154185

[6] Marcello Di Martino, Styliani Avraamidou, Efstratios N. Pistikopoulos. A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants. Membranes 2022, 12, 199. https://doi.org/10.3390/membranes12020199