Evolutionary multi-objective optimization of the design and operation of water distribution network: total cost vs. reliability vs. water quality

An expanded rehabilitation of the hypothetical water distribution network of Anytown, USA is considered. As well as pipe rehabilitation decisions, tank sizing, tank siting and pump operation schedules are considered as design variables. Inclusion of pump operation schedules requires consideration of water system operation over the demand pattern period. Design of distribution storage facilities involves solving numerous issues and trade-offs such as locations, levels and volume. This paper investigates the application of multi-objective evolutionary algorithms in the identification of the pay-off characteristic between total cost, reliability and water quality of Anytown9s water distribution system. A new approach is presented for formulation of the model. To provide flexibility, the network must be designed and operated under multiple loading conditions. The cost of the solution includes the capital costs of pipes and tanks as well as the present value of the energy consumed during a specified period. Optimization tends to reduce costs by reducing the diameter of, or completely eliminating, pipes, thus leaving the system with insufficient capacity to respond to pipe breaks or demands that exceed design values without violating required performance levels. Here a resilience index is considered as a second objective to increase the hydraulic reliability and the availability of water during pipe failures. Considering reliability as one of the objectives in the optimization process will decrease the level of vulnerability for the solutions and therefore will result in robust networks. However, oversized distribution mains and storage tanks will have adverse effects on water age with negative effects on water quality due to low flow velocity and little turnover, respectively. Therefore, another objective in the design and operation of distribution systems with storage facilities is the minimization of residence time, thus minimizing deterioration in water quality, which is directly associated with the age of water. Residence time must include not only the time in tanks but also the travel time before and after the water9s entry into the storage facilities. The residence time of the water in the network is considered as a surrogate measure of water quality. Results are presented for the pay-off characteristics between total cost, reliability and water quality, for 24 h design and five loading conditions.

[1]  Rasheed Ahmad Hydraulic Design of Water Distribution Storage Tanks , 2005 .

[2]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[3]  Zoran Kapelan,et al.  Multiobjective sampling design for water distribution model calibration , 2003 .

[4]  Richard de Neufville,et al.  Systems Analysis of Water Distribution Networks , 1971 .

[5]  Dragan Savic,et al.  Improved design of “Anytown” distribution network using structured messy genetic algorithms , 1999 .

[6]  Robert M. Clark,et al.  Algorithm for Mixing Problems in Water Systems , 1985 .

[7]  Godfrey A. Walters,et al.  Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply , 1997, Evolutionary Computing, AISB Workshop.

[8]  Dragan Savic,et al.  Trade-off between Total Cost and Reliability for Anytown Water Distribution Network , 2005 .

[9]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[10]  Larry W. Mays,et al.  Battle of the network models: Epilogue , 1987 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  L Reis,et al.  Multi-objective optimization to the rehabilitation of a water distribution network , 2003 .

[13]  John W. Baugh,et al.  Genetic Algorithm Search for Least Cost Design of Looped Pipe Networks Using Age as a Quality Surrogate and Different Levels of Redundancy , 2001 .

[14]  Ezio Todini,et al.  Looped water distribution networks design using a resilience index based heuristic approach , 2000 .

[15]  Larry W. Mays,et al.  Optimization Model for Water Distribution System Design , 1989 .

[16]  Eckart Zitzler,et al.  Evolutionary multi-objective optimization , 2007, Eur. J. Oper. Res..

[17]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[18]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[19]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[20]  Angus R. Simpson,et al.  Optimum Design and Operation of Pumped Water Distribution Systems , 1994 .

[21]  Moses O. Tadé,et al.  Multi-objective genetic algorithm for optimal scheduling of chlorine dosing in water distribution systems , 2003 .

[22]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Graeme C. Dandy,et al.  Optimum Rehabilitation of a Water Distribution System Considering Cost and Reliability , 2001 .

[25]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[26]  R. Farmani,et al.  Evolutionary multi-objective optimization in water distribution network design , 2005 .

[27]  Dragan Savic,et al.  WATER NETWORK REHABILITATION WITH STRUCTURED MESSY GENETIC ALGORITHM , 1997 .