Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools.

The overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals.

Real-Time Monitoring and Control of Urea Hydrolysis in Cyber-Enabled Nonwater Urinal System.

This research used a cyber-physical system (CPS) to monitor and control the extent of urea hydrolysis in nonwater urinals. Real-time pH and conductivity data were used to control urea hydrolysis inhibition under realistic restroom conditions with acetic acid addition. Variable urination frequencies and urination volumes were used to compare three conditions that affect the progression of urea hydrolysis.