Real-time control of the salt front in a complex, tidally affected river basin

摘要

The U.S. Geological Survey (USGS) participated in comparing artificial neural networks (ANN's) to deterministic models of transport and water quality phenomena of an estuary in Charleston, SC. The models were developed from real-time data from a gauging network operated by the USGS. The results favored the ANN's accuracy and reduced development time. They could spatially interpolate between gauging stations to predict the location of the freshwater/saltwater interface, called the "salt front". The salt front location depends on the interaction of freshwater flowing downstream from a hydroelectric dam and tidal forcing of saltwater upstream. Government regulations conservatively control dam releases to prevent saltwater migrating into a freshwater reservoir, but sub-optimizes the commercial operation of the darn. This paper describes an alternative control approach using an ANN model of the "gain" between the freshwater releases and the specific conductivity, used to estimate salinity, near the reservoir. A scheme for implementing the model in a real-time control system is also described.