Modelling Groundwater Arsenic Contamination with Model Predictive Control
Abstract
A robust characterisation of groundwater arsenic contamination is a prerequisite for the safe expansion of supply chain logistics. The experimental programme spans several configurations typical of supply chain logistics. Over a period of 8 months, the monitored system exhibited stable and predictable behaviour. Further validation across a broader range of conditions is recommended before large-scale deployment.
Keywords:
groundwater arsenic contaminationmodel predictive controlcognitive radiosupport vector machineblockchain
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