Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that can be used in optimization of the operating strategy. This paper proposes a new algorithm for online learning of the characteristics of the compressors using Gaussian Processes. The performance of the new approximation is shown in a case study with three compressors. The case study shows that Gaussian Processes accurately capture the characteristics of compressors even if no knowledge about the characteristics is initially available. The results show that the flexible nature of Gaussian Processes allows them to adapt to the data online making them amenable for use in real-time optimization problems.
翻译：设计压缩机站的最佳操作战略取决于压缩机站的压缩机特性知识。随着压缩机特性随着时间和使用的变化而变化,有必要提供精确的模型,说明可用于优化操作战略的特性。本文件提出了使用高山进程在线学习压缩机特性的新算法。在与三个压缩机进行的案例研究中显示了新近似的性能。案例研究显示,高山进程准确地捕捉了压缩机的特性,即使最初没有关于这些特性的知识。结果显示,高山进程具有灵活性,能够适应在线数据,使其适应实时优化问题。
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