A multiobjective approach is used here to optimize design and operation of the C-Town pipe network, searching for trade-off solutions between (1) installation cost, (2) operational cost, and (3) cost of the pressure-reducing valves. Due to the large number of decisional variables and to the complexity of the constraints considered, the optimization problem was tackled in five steps: (1) identification of some feasible (on the basis of the many constraints) first attempt solutions; (2) application of a multiobjective genetic algorithm to the 2D optimization problem with objective functions 1 and 2, in order to obtain optimal trade-off solutions between the installation cost and operational cost, without considering the installation of pressure-reducing valves; (3) application of the multiobjective genetic algorithm to the optimization problem with objective functions 2 and 3 for each of the solution selected at the end of Step 2, in order to assess how the operational cost can decrease thanks to the installation and operation of pressure-reducing valves; (4) derivation of the 3D Pareto surface by grouping the solutions found at the end of Steps (2) and (3). A solution was extracted from the 3D Pareto surface of optimal solutions following some specific criteria. This solution was then further refined (Step 5) in order to allow for variable settings of the pressure-reducing valves installed and to make it compliant with the battle guidelines concerning leakage modeling.

Multistep Approach for Optimizing Design and Operation of the C-Town Pipe Network Model

CREACO, ENRICO FORTUNATO;
2016-01-01

Abstract

A multiobjective approach is used here to optimize design and operation of the C-Town pipe network, searching for trade-off solutions between (1) installation cost, (2) operational cost, and (3) cost of the pressure-reducing valves. Due to the large number of decisional variables and to the complexity of the constraints considered, the optimization problem was tackled in five steps: (1) identification of some feasible (on the basis of the many constraints) first attempt solutions; (2) application of a multiobjective genetic algorithm to the 2D optimization problem with objective functions 1 and 2, in order to obtain optimal trade-off solutions between the installation cost and operational cost, without considering the installation of pressure-reducing valves; (3) application of the multiobjective genetic algorithm to the optimization problem with objective functions 2 and 3 for each of the solution selected at the end of Step 2, in order to assess how the operational cost can decrease thanks to the installation and operation of pressure-reducing valves; (4) derivation of the 3D Pareto surface by grouping the solutions found at the end of Steps (2) and (3). A solution was extracted from the 3D Pareto surface of optimal solutions following some specific criteria. This solution was then further refined (Step 5) in order to allow for variable settings of the pressure-reducing valves installed and to make it compliant with the battle guidelines concerning leakage modeling.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1144782
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