Abstract Non-linear and non-Gaussian properties are challenging topics in the soft sensor modeling of chemical processes, and fluctuations in the environmental conditions of chemical plants will also affect the accuracy of soft sensor models. This paper proposes an adaptive soft sensor method of D-vine copula quantile regression (aDVQR). In the modeling process, a sparse vine model is established using the Bayesian information criterion. Then, the conditional quantile function value of the specified quantile can be obtained via the recursive nesting method by the h function. An online model updating system based on the aDVQR model is also proposed, and an adaptive soft sensor model is established. The proposed adaptive soft sensor method can successfully approximate the non-linear and non-Gaussian relationships between variables and adapt to unstable environments. Finally, a numerical example and an example of the ethylene industry are used to verify the effectiveness of the proposed method.

中文翻译：

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复杂化学过程中D-vine Copula分位数回归的自适应软传感器方法

摘要 非线性和非高斯特性是化学过程软传感器建模中具有挑战性的课题，化工厂环境条件的波动也会影响软传感器模型的准确性。本文提出了一种D-vine copula分位数回归（aDVQR）的自适应软传感器方法。在建模过程中，采用贝叶斯信息准则建立稀疏藤蔓模型。然后，可以通过h函数通过递归嵌套的方法获得指定分位数的条件分位数函数值。提出了一种基于aDVQR模型的在线模型更新系统，建立了自适应软传感器模型。所提出的自适应软传感器方法可以成功地逼近变量之间的非线性和非高斯关系，并适应不稳定的环境。最后，通过数值算例和乙烯工业算例验证了所提方法的有效性。