Optimization of Pump Operations in a Complex Water Supply Network: New Genetic Algorithm Frameworks

Optimization of Pump Operations in a Complex Water Supply Network: New Genetic Algorithm Frameworks

D. De Wrachien S. Mambretti E. Orsi

State University of Milano, Italy.

Politecnico di Milano, Italy

Page: 
79-88
|
DOI: 
https://doi.org/10.2495/SDP-V12-N1-79-88
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

In previous papers, a simple genetic algorithm (GA) was developed for the optimization of pump operations in water-distribution networks. Its application at the water supply network of Milano showed the possibility of a great improvement of its performance in terms of both energy and economic savings. In the present paper is now investigated the possibility of using different and improved GAs to obtain better results. Improvements concerned the description of the pump conditions with a real number (and therefore in continuous form) and the introduction of elitism and of a slightly modified form of mutation. Simulations were obviously performed with reference to the same model under the same assumptions of the previous papers. Results showed significant improvements in the passage from a discrete to a continuous description of the pumps functioning and a slight improvement using elitism and no differences using mutation. The latter result might need some more research: mutation is introduced to enlarge the space in which the ‘individuals’ perform their search, and there is the need to understand whether this little improvement is due to the poor performance of this mutation or instead, because the space of search is already well defined. The need for more in-depth investigations is also investigated in the present paper.

Keywords: 

energy saving, genetic algorithms, optimization, water distribution

  References

[1] Back, T., Fogel, D.B. & Michalewicz, Z. (eds.), Handbook of Evolutionary Computation, Institute of Physics publishing & Oxford University Press: New York, 1997.

[2] Holland, J.H., Outline for a logical theory of adaptive systems. Journal of The Association for Computing Machinery, 3, pp. 297–314, 1962. http://dx.doi.org/10.1145/321127.321128

[3] Holland, J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press: Ann Arbor, Mich., 1975.

[4] Rechenberg, I., Evolutionstrategie: optimierung technisher systeme nach prinzipien der biologischen evolution. Frommann-Hoolzboog Verlag: Stuttgart, 1973. (in German)

[5] Schewefel, H.P., Numerical Optimization of Computer Models, John Wiley and Sons: New York, 1981.

[6] Fogel, L., Owens, A. & Walsh, M., Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, Inc.: New York, 1966Koza, 1992.

[7] Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley: Reading, MA, 1989.

[8] Golberg, D.E. & Kuo, C.H. Genetic algorithms in pipeline optimization. Iournal of Computing in Civil Engineering, 1(2), pp. 128–141, 1987. http://dx.doi.org/10.1061/(ASCE)0887-3801(1987)1:2(128)

[9] Simpson, A.R., Dandy, G.C. & Murphy, L.J., Genetic algorithms compared to other techniques for pipe optimization. Journal of Water Resources Planning and Management, ASCE, 120(4), pp. 423–443, 1994. http://dx.doi.org/10.1061/(ASCE)0733-9496(1994)120:4(423)

[10] Savic, D.A. & Walters, G.A., Genetic algorithm for least-cost design of water distribution networks. Journal of Water Resources Planning and Management, ASCE, 123, pp. 67–77, 1997. http://dx.doi.org/10.1061/(ASCE)0733-9496(1997)123:2(67) 

[11] Alperovits, E. & Shamir, U., Design of optimal water distribution systems. Water Resources Research, 13(6), pp. 885–900, 1977. http://dx.doi.org/10.1029/WR013i006p00885

[12] Fujiwara, O. & Khang, D.B., A two-phase decomposition method for optimal design of looped water distribution networks. Water Resources Research, 26(4), pp. 539–549, 1990. http://dx.doi.org/10.1029/WR026i004p00539

[13] Schaake, J.C. & Lai, D., Linear Programming and Dynamic Programming Application to Water Distribution Network Design. Rep. No. 116, Department of Civil Engineering, MIT, Cambridge, 1969.

[14] Mackle, G., Savic, D.A. & Walters, G.A. Application of genetic algorithms to pump scheduling for water supply. Proceedings of Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA ‘95, IEE, London, pp. 400–405, 1995.

[15] Atkinson, R., van Zyl, J.E., Walters, G.A. & Savic, D.A., Genetic algorithm optimization of level-controlled pumping station operation. Water Network Modelling for Optimal Design and Management, Centre for Water Systems, Exeter, UK, pp. 79–90, 2000.

[16] De Schaetzen, W.B.F., Savic, D.A. & Waltres, G.A. A genetic algorithm approach to pump scheduling in water supply system. Hydroinformatics, 1998.

[17] Illich, N. & Simovic, S.P., Evolutionary algorithm for minimization of pumping cost. Journal of Computing in Civil Engineering, ASCE, 12, pp. 232–240, 1998. http://dx.doi.org/10.1061/(ASCE)0887-3801(1998)12:4(232)

[18] van Zyl, J., Savic, D.A. & Walters, G.A., Operational optimization of water distribution systems using a hybrid genetic algorithm method. Journal of Water Resources Planning and Management, 130(2), pp. 160–170, 2004. http://dx.doi.org/10.1061/(ASCE)0733-9496(2004)130:2(160)

[19] Prasad, T. D. & Park, N.S., Multiobjective genetic algorithms for design of water distribution networks. Journal of Water Resources Planning and Management, 130(1), pp. 73–82, 2004. http://dx.doi.org/10.1061/(ASCE)0733-9496(2004)130:1(73)

[20] Farmani, R., Savic, D.A. & Walters, G.A., Evolutionary multi-objective optimization in water distribution network design. Engineering Optimization, 37(2), pp. 167–183, 2005. http://dx.doi.org/10.1080/03052150512331303436

[21] Rao, Z. & Salomons, E., Development of a real-time, near optimal control process for water-distribution networks. Journal of Hydroinformatics, 9(1), pp. 25–37, 2007. http://dx.doi.org/10.2166/hydro.2006.015

[22] Mambretti, S., Optimization of the pumping station of Milano water supply network with genetic algorithms, 3rd International Conference on Energy and Sustainability, Alicante, Spain, pp. 185–194, 11–13 April 2011.

[23] Mambretti, S. & Orsi, E., Optimization of Pumping Stations in Complex Water Supply Networks through Evolutionary Computation Methods, Accepted for publication in the Journal of American Water Works Association, scheduled for February 2016. http://dx.doi.org/10.2495/esus110161

[24] Mundo, D. & Yan, H.S., Kinematic optimization of ball-screw transmission mechanisms. Mechanism and Machine Theory, 42, pp. 34–47, 2007. http://dx.doi.org/10.1016/j.mechmachtheory.2006.02.002

[25] Behandish, M. & Wu, Z.Y., Concurrent pump scheduling and storage level optimization using meta-models and evolutionary algorithms. Procedia Engineering, 70, pp. 103– 112, 2014. http://dx.doi.org/10.1016/j.proeng.2014.02.013

[26] Motta, V., L’acquedotto di Milano, Comune di Milano, 1989. (in Italian)