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.
@article{arxiv.1711.04988,
title = {Concurrent Pump Scheduling and Storage Level Optimization Using Meta-Models and Evolutionary Algorithms},
author = {Morad Behandish and Zheng Yi Wu},
journal= {arXiv preprint arXiv:1711.04988},
year = {2017}
}
Comments
12th International Conference on Computing and Control for the Water Industry (CCWI'2013)