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基于支持向量机的减压塔产品粘度预估Product Visco
李明
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原油炼制中减压塔侧线产品是精制润滑油的原料，其粘度测量对生产有重要意义。通
过统计分析得出了影响侧线产品粘度的主要因素，并采用支持向量机回归方法建立粘度软测量模型。针对支持向量机训练参数确定问题，提出了采用差分进化算法的搜索策略，使模型训练参数的调整过程按预定目标自动快速优化。所构建的粘度软测量模型预报精度较高，趋势跟踪性能良好。
关键词：软测量；减压塔；粘度；支持向量机；差分进化；
Abstract: The measurement of viscosity is important for the side-draw product quality of vacuum distillation column in oil refinery. The main factors, which influence the viscosity, were analyzed by statistic methods. Then soft-sensor models were developed based on support vector regression. A search method based on differential evolution was proposed to select proper training parameters. Using this strategy, the parameters can be optimized automatically and efficiently. The developed model got high accuracy and good trend tracking performance on the real industrial data.
Keywords: soft sensor; vacuum distillation column; viscosity; support vector machine;
differential evolution;
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