Abstract Data driven soft sensors have found widespread applications in chemical processes for predicting those important yet difficult-to-measure quality variables. In the vast majority of chemical processes, relationships among primary and secondary variables are nonlinear, and process data inherently contain uncertainties and present strongly non-Gaussian characteristics. In addition, labeled samples are often scarce due to certain technical or economical difficulties. These process and data characteristics impose challenges on high-accuracy soft sensors. To deal with these issues, this paper proposes a soft sensing approach referred to as the semisupervised Dirichlet process mixture of Gaussians (SsDPMG). In the SsDPMG, a fully Bayesian model structure is first designed to enable semisupervised tasks that are suitable for regression applications. Subsequently, a Bayesian learning procedure for the SsDPMG is developed based on variational inference framework, where information contained in both labeled and unlabeled samples are extracted. Case studies are carried out on one numerical example and two real-life chemical processes to evaluate the performance of the proposed approach. The results demonstrate that the SsDPMG is an effective soft sensing approach with promising application foreground.

中文翻译：

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基于高斯半监督狄利克雷过程混合的化学过程质量变量预测

摘要 数据驱动的软传感器已广泛应用于化学过程中，用于预测那些重要但难以测量的质量变量。在绝大多数化学过程中，主要和次要变量之间的关系是非线性的，过程数据固有地包含不确定性并呈现出强烈的非高斯特征。此外，由于某些技术或经济困难，标记的样本通常很少。这些过程和数据特性对高精度软传感器提出了挑战。为了解决这些问题，本文提出了一种软感知方法，称为高斯半监督狄利克雷过程混合（SsDPMG）。在 SsDPMG 中，完全贝叶斯模型结构首先被设计为支持适用于回归应用的半监督任务。随后，基于变分推理框架开发了 SsDPMG 的贝叶斯学习程序，其中提取了标记和未标记样本中包含的信息。对一个数值示例和两个现实生活中的化学过程进行了案例研究，以评估所提出方法的性能。结果表明，SsDPMG 是一种有效的软传感方法，具有广阔的应用前景。对一个数值示例和两个现实生活中的化学过程进行了案例研究，以评估所提出方法的性能。结果表明，SsDPMG 是一种有效的软传感方法，具有广阔的应用前景。对一个数值示例和两个现实生活中的化学过程进行了案例研究，以评估所提出方法的性能。结果表明，SsDPMG 是一种有效的软传感方法，具有广阔的应用前景。