Optimization of groundwater supply under consideration of capacity expansion in unconfined aquifer

Liang-Jeng Chang*, Ming Sheng Yeh, Yu W. Chen, Chin Tsai Hsiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Groundwater resource plays a vital role in regional water resources in diverse ways. Using groundwater in a region water supply generally depends on a large amount of pumping wells, i.e., a wells network. Conventional designs determine the capacity of a network system once for all and the network system is designed to fulfill the maximum water demand. Since the water demand is used to increase in time, the system may be over design initially. Therefore, this work proposes a novel optimal capacity-expansion model capable of determining an optimal schedule to expand the system capacity according to increasing water demand. While combining the Genetic Algorithm (GA) and Constrained Differential Dynamic Programming (CDDP) to solve the optimization problem, the proposed model assumes a 2-D groundwater flow for a confined and unconfined aquifer. The main structure of the hybrid algorithm is GA, in which each chromosome represents a possible network design and its expansion schedule. The fixed cost of each chromosome can be computed easily. The DDP then solves the optimal pumping and injecting scheme and, finally, evaluates the optimal operating costs associated with each chromosome. Simulation results indicate that, under the same annul interest rate and demand, the capacity-expansion model can save more present value of the total cost than the conventional design can that determines the system capacity initially. Furthermore, the benefits of using the capacity-expansion model increase with a rising interest rate. Our results further demonstrate that the proposed model is highly promising for use in facilitating a cost-efficient design of well system for regional groundwater supply.

Original languageEnglish
Pages (from-to)35-43
Number of pages9
JournalJournal of the Chinese Institute of Civil and Hydraulic Engineering
Volume19
Issue number1
StatePublished - 1 Mar 2007

Keywords

  • Dynamic optimal control
  • Genetic algorithm
  • Unconfined aquifer

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