Abstract
Abstract This paper suggests a pipeline project optimization approach that compares alternatives with different life spans. The average inflation rate is used to project the future maintenance, operation and replacement costs. The average interest rate is used to express all the costs in Equivalent Real Annual Cost (ERAC), which is the correct cost form to compare alternatives with different life spans. The pipe diameter, material, pressure rating, surge tank size, and inlet/outlet resistances are the decision variables. A software was compiled with a commercial pipeline software to generate all the possible design alternatives based on the decision variables. Pipe initial cost as well as operation and maintenance costs are computed for each design alternative. The alternative with the least ERAC value is the optimum one. It was found that the approach can lead to substantial savings in pipeline projects cost. For pipes 800 mm in diameter or larger, and when selecting the optimum diameter, savings are between 23 and 27% in the total project cost. When imposing certain pipe material savings in overall cost will be 8.5, 16.3 and 31.3% for ductile iron, GRP and mild steel pipe material, respectively.
Highlights
Pipeline design is typically accomplished for steady-state flow conditions, with water hammer protection devices representing a small portion of the overall pipeline cost
Given the huge expenditure involved in building pipelines, cost optimization is an important issue in the global construction industry
A Genetic algorithms (GAs) was implemented to search for the optimum cost of the pipeline material, installation, and maintenance
Summary
Pipeline design is typically accomplished for steady-state flow conditions, with water hammer protection devices representing a small portion of the overall pipeline cost. This approach has proven to be inaccurate ( Jung & Karney 2006). A few studies have addressed the optimization of pipe networks for steady-state and transient flow conditions based on pipe size only, that is, without the use of water hammer protection devices (Djebedjian et al 2005; Afshar 2006). Genetic algorithms (GAs) and particle swarm optimization (PSO) have been used to optimize the use of surge protection devices with respect to their number, sizes, and locations (Jung & Karney 2006). Six different scenarios for using a predetermined number of devices (e.g., two surge tanks, one surge tank, and one pressure-relief valve, or three pressure-relief valves) have been considered, and GAs and PSO techniques have been used to find the optimal sizes and locations of the devices
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