Hamiltonian Monte Carlo-Based D-Vine Copula Regression Model for Soft Sensor Modeling of Complex Chemical Processes
Hamiltonian Monte Carlo-Based D-Vine Copula Regression Model for Soft Sensor Modeling of Complex Chemical Processes
复制标题
基于哈密顿蒙特卡罗的 D-Vine Copula 回归模型，用于复杂化学过程的软测量建模
DOI：
10.1021/acs.iecr.9b05370
复制
发表时间：
2020
影响因子：
4.2
通讯作者：
Shaojun Li
中科院分区：
文献类型：
--
作者：
Jianeng Ni;Yang Zhou;Shaojun Li
化工过程的非线性和非高斯特性是化工过程软测量建模的难点。本文提出了一种基于Hamiltonian Monte Carlo（HMC）抽样策略（HMCCR）的D-vine Copula回归方法。在数据预处理过程中，采用了擀面杖单调变换方法，保证了数据具有单调关系。然后，基于辅助变量建立D-vine copula模型，得到关键变量的条件概率密度。使用HMC方法计算查询数据集的期望值、方差和预测不确定性。所提出的回归方法可以成功地近似的非线性和非高斯的输出和输入变量之间的关系，使用藤copula函数。此外，我们还提出了一种基于HMCCR模型的补充采样策略，以提醒操作员补充人工分析。通过两个工业实例验证了该方法的有效性和性能。
Nonlinear processes and non-Gaussian properties are challenging subjects for soft sensor modeling of chemical processes. In this paper, we propose a D-vine copula regression method based on a Hamiltonian Monte Carlo (HMC) sampling strategy (HMCCR). In the data pretreatment process, the rolling pin monotonic transformation method is used to ensure that the data have a monotonic relationship. Subsequently, a D-vine copula model is established to obtain the conditional probability density of the key variables based on the auxiliary variables. The expected value, the variance, and the prediction uncertainty of the query data set are calculated using the HMC method. The proposed regression method can successfully approximate the nonlinear and non-Gaussian relationship between the output and input variables using the vine copula function. In addition, we also propose a supplementary sampling strategy based on the HMCCR model to remind operators to supplement the manual analysis. The validity and performance of the proposed method are demonstrated using two industrial examples.