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Water quality monitoring for coupled food, energy, and water systems
Author(s) -
Mickelson Alan,
Tsvankin Daniel
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12789
Subject(s) - water quality , quality (philosophy) , stressor , energy (signal processing) , computer science , system dynamics , real time computing , environmental science , artificial intelligence , ecology , mathematics , medicine , clinical psychology , philosophy , statistics , epistemology , biology
The focus is on generating real time data for predictive models of food, energy, and water (FEW) systems. It is hypothesized that stressors affecting a FEW system universally impact the water quality of the system. Conversely, fine grain temporal and spatial data describing the water quality of a FEW system can be used to locate system stressors. A high level, predictive model of a FEW system is presented. It is noted that the dynamics of the system are driven by steady state operating characteristics and system stressors. An approach to generation of fine grained spatial temporal water quality data is discussed and an archetypical element of a sensor array is fabricated. The archetypical micro‐controlled sensor element is experimentally evaluated. The results are applied to determining the characteristics necessary of an element of a sensor arrays that is to be used in the prediction of the dynamics of a FEW system. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 165–171, 2018