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    张剑超, 杜文莉, 覃水. 基于新型自适应采样算法的催化重整过程代理模型[J]. 华东理工大学学报(自然科学版), 2019, 45(6): 928-937. DOI: 10.14135/j.cnki.1006-3080.20180915001
    引用本文: 张剑超, 杜文莉, 覃水. 基于新型自适应采样算法的催化重整过程代理模型[J]. 华东理工大学学报(自然科学版), 2019, 45(6): 928-937. DOI: 10.14135/j.cnki.1006-3080.20180915001
    ZHANG Jianchao, DU Wenli, QIN Shui. Surrogate Model of Catalytic Reforming Process Based on a New Adaptive Sampling Algorithm[J]. Journal of East China University of Science and Technology, 2019, 45(6): 928-937. DOI: 10.14135/j.cnki.1006-3080.20180915001
    Citation: ZHANG Jianchao, DU Wenli, QIN Shui. Surrogate Model of Catalytic Reforming Process Based on a New Adaptive Sampling Algorithm[J]. Journal of East China University of Science and Technology, 2019, 45(6): 928-937. DOI: 10.14135/j.cnki.1006-3080.20180915001

    基于新型自适应采样算法的催化重整过程代理模型

    Surrogate Model of Catalytic Reforming Process Based on a New Adaptive Sampling Algorithm

    • 摘要: 催化重整装置是石油加工的重要装置之一,直接使用机理模型对其进行优化耗时较长。代理模型方法能够有效地对机理模型进行近似,而采样方法对代理模型的精度有很大影响。提出了一种新的自适应采样算法−基于最近邻和马氏距离的自适应采样算法。该算法从采样方法的全局搜索能力与局部搜索能力出发,通过求解优化问题在关键信息区域中获取样本点。采用7个测试函数进行测试,结果表明该算法能够选取对代理模型精度影响较大的采样点,从而有效地提升代理模型的精度。针对催化重整过程的关键质量指标建立了相应的代理模型,结果表明该算法能够很好地处理实际工程中的问题。

       

      Abstract: Catalytic reforming unit is one of the most important devices in petroleum processing. However, because of its high dimensionality, complex mechanism, and complex working conditions, catalytic reforming unit is time-consuming for the optimization via direct using the mechanism model. Aiming at these shortcoming, researchers have proposed the concept of surrogate model, which can effectively approximate mechanism models. The surrogate model usually includes two steps: firstly, a series of sampling points are generated in the input space and the true response values are obtained; secondly, the corresponding surrogate model is established through the sampling point and its real response value. Apparently, the sampling strategy has direct impact on the speed and accuracy of surrogate model. Hence, this paper proposes a new adaptive sampling algorithm, termed as adaptive sampling algorithm-based nearest neighbor and Mahalanobis distance (ASA-NNMD). Through global exploration and local exploitation of sampling strategy, the proposed algorithm can obtain the sample point in the key information area. It is shown via seven test functions that ASA-NNMD can select sampling points of greater impact on the accuracy of surrogate model so that it can effectively improve the accuracy of surrogate model. Finally, the corresponding surrogate models are established for these key quality indexes of catalytic reforming process via four inlet temperatures, feed load, and hydrogen / oil ratio.

       

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