Concurrent Pump Scheduling and Storage Level Optimization Using Meta-Models and Evolutionary Algorithms
This work addresses operational efficiency for water distribution systems, but it is incremental as it combines existing methods like ANN and GA.
The paper tackled the challenge of fast pump scheduling in complex water distribution systems by optimizing pump operation and tank levels simultaneously, resulting in a 10-15% reduction in daily cost while limiting pump switches to below 4 per day.
In spite of the growing computational power offered by the commodity hardware, fast pump scheduling of complex water distribution systems is still a challenge. In this paper, the Artificial Neural Network (ANN) meta-modeling technique has been employed with a Genetic Algorithm (GA) for simultaneously optimizing the pump operation and the tank levels at the ends of the cycle. The generalized GA+ANN algorithm has been tested on a real system in the UK. Comparing to the existing operation, the daily cost is reduced by about 10-15%, while the number of pump switches are kept below 4 switches-per-day. In addition, tank levels are optimized ensure a periodic behavior, which results in a predictable and stable performance over repeated cycles.