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Application of Constrained Multi-objective Evolutionary Algorithm in Multi-Source Compressed-air Pipeline Optimization Problems

机译:约束多目标进化算法在多源压缩空气管线优化问题中的应用

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To meet the request of manufacture, several compressor stations usually run at the same time. Decreasing the output pressure of compressor station is one of the major methods to reduce the power utilized by the motors of the compressors. Due to the interaction of several compressor stations with each other, how to set the output pressure of each compressor station becomes a big problem. This paper proposes the Constrained Multi-objective Optimization of Multi-Source Compressed-air Pipeline Optimization Problems (CMO-MSCPOPs) in compressed-air transmission networks of process industries. The problem formulation involves the minimization of the output pressure of each compressor station. Constraints associated with compressed-air flow rate and compressor stations guarantee the work of each downstream process. In case studies, the model is divided into two topology forms. The optimization of the model is performed using NSGA-II. The solution obtained is a set of Pareto solutions from which a decision making process is highlighted to select a specific preferred solution. Aiming to illustrate the performance of the proposed approach, the tool is applied to two typical network examples considering two compressor stations.
机译:为了满足制造要求,几个压缩机站通常同时运行。减小压缩机站的输出压力是减少压缩机电机使用的功率的主要方法之一。由于彼此的几个压缩机站的相互作用,如何设置每个压缩机站的输出压力变为大问题。本文提出了过程行业压缩空气传输网络中多源压缩空气管道优化问题(CMO-MSCPOP)的约束多目标优化。问题制定涉及最小化每个压缩机站的输出压力。与压缩空气流速和压缩机站相关联的约束保证了每个下游过程的工作。在研究中,该模型分为两种拓扑形式。使用NSGA-II进行模型的优化。获得的溶液是一组Pareto解决方案,其中突出了决策过程以选择特定的优选解决方案。旨在说明所提出的方法的性能,该工具应用于考虑两个压缩机站的两个典型的网络示例。

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