Literature DB >> 30320306

Towards a Quantitative Food-Energy-Water Nexus Metric to Facilitate Decision Making in Process Systems: A Case Study on a Dairy Production Plant.

Styliani Avraamidou1,2, Aaron Milhorn1,3, Owais Sarwar1,4, Efstratios N Pistikopoulos1.   

Abstract

While the importance of the Food-Energy-Water Nexus (FEW-N) has been widely accepted, a holistic approach to facilitate decision making in FEW-N systems, along with a quantitative index assessing the integrated FEW-N performance is rather lacking. In this work, we propose a FEW-N metric along with a framework to facilitate decision making for FEW-N process systems through a FEW-N integrated approach. The framework and metric are illustrated through a case study on a dairy production and processing plant. The dairy industry is a significant user of water and energy, with water being a top issue for most dairy industries and organizations worldwide. Following the framework, we develop a mixed-integer scheduling model, with alternative pathways, that faithfully replicated the major food, energy, and water aspects of a real cottage-cheese production plant. Using the developed FEW-N metric we were able to optimize the cottage-cheese plant process and observe different trade-offs between the FEW-N elements.

Entities:  

Keywords:  Food-Energy-Water Nexus; Multi-Objective Optimization

Year:  2018        PMID: 30320306      PMCID: PMC6177266          DOI: 10.1016/B978-0-444-64235-6.50071-1

Source DB:  PubMed          Journal:  ESCAPE


  1 in total

1.  A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants.

Authors:  Marcello Di Martino; Styliani Avraamidou; Efstratios N Pistikopoulos
Journal:  Membranes (Basel)       Date:  2022-02-09
  1 in total

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