Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China.
Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China.
ISA Trans. 2018 Feb;73:257-267. doi: 10.1016/j.isatra.2018.01.003. Epub 2018 Jan 6.
Due to prior knowledge being often unavailable in practice, a multi-block strategy totally based on data-driven analytics is an appropriate alternative for plant-wide processes. However, most recent multi-block methods are relatively vague or insufficient for dividing up the process space and lack the comprehensive fault information for quality-related monitoring. This work intends to develop a more reasonable multi-block method and demonstrate the negative impacts of quality-unrelated variables. Both motivations are entirely dependent on the correlation between variables. A major innovation is to determine those independent or related sets of variables, and to provide a more precise indication for those quality-related faults. Sub-blocks with related variables are each modeled by the KPCA, and the rest of the independent variables are treated as an input for a SVDD model. Finally, all of the statistical indicators are aggregated into a single statistic through Bayesian inference. The benefits of the proposed multi-block scheme (MKPCA-SVDD) are elaborated on in detail using numerical simulation, TE benchmark and industrial p-xylene oxidation process.
由于在实际应用中往往缺乏先验知识，因此基于数据驱动分析的多块策略是全流程的合适替代方案。然而，最近的大多数多块方法对于划分过程空间相对模糊或不足，并且缺乏与质量相关的监测的全面故障信息。本工作旨在开发一种更合理的多块方法，并展示与质量无关变量的负面影响。这两个动机完全取决于变量之间的相关性。一项重大创新是确定那些独立或相关的变量集，并为那些与质量相关的故障提供更精确的指示。具有相关变量的子块由 KPCA 建模，其余独立变量则作为 SVDD 模型的输入。最后，所有统计指标都通过贝叶斯推理聚合到单个统计数据中。通过数值模拟、TE 基准和工业对二甲苯氧化过程详细阐述了所提出的多块方案 (MKPCA-SVDD) 的优势。
