Abstract
This paper utilizes a stochastic optimization approach using genetic algorithms, for conducting rigorous pipe size sensitivity assessments onto the design of pressure relief networks. By sampling high performance candidates, only the finest options can survive. The pressure relief network system that was investigated in this work was previously reported in literature. The problem is constrained and involves minimizing a cost objective function that evaluates the overall network performance, in which the best pipe size combination should be selected for each segment within the network. The overall goal of this paper was to seek cost-effective designs for the pressure relief piping system by exploring different ranges of pipe diameters that are available for each segment in the network and comparing how the overall design of the system is affected, when the number of pipe size options to select from is varied.
Highlights
Genetic algorithms are stochastic optimization techniques that can seek well-performing solutions in an evolutionary manner, by sampling regions that possess high performance probabilities where only the fittest options survive [1]
A pressure relief network design problem has been attempted in this work by applying a practical search-based optimization via genetic algorithms
The problem involves minimizing a cost objective that evaluates the network design performance according to the sizing of the pipes
Summary
Genetic algorithms are stochastic optimization techniques that can seek well-performing solutions in an evolutionary manner, by sampling regions that possess high performance probabilities where only the fittest options survive [1]. Instead of designing specific algorithms for solving particular types of problems, he introduced effective techniques for importing the mechanisms of natural adaptation into computer systems, by closely relating this natural phenomenon in biological evolution to computational behaviour [6]. His approach involved bitstring and real-valued representations to optimize systems using a genetically−inspired search methodology, in which the algorithm moves from one population of solutions to a new one, according to the laws of natural selection. A representative strategy is often needed for choosing the best performing
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