基于多数据空间全潜结构映射的化工过程性能评估方法

针对化工过程运行状态在线评估的问题，提出多数据空间全潜结构映射（multi-space total projection to latent structures，MsT-PLS）性能评估方法。该方法采用“离线建模，在线评估”的评估策略。首先对历史多数据输入空间进行全面分解，结合多数据空间基向量提取方法，剔除多数据输入空间中与质量变量无关信息的干扰。在与质量变量相关的多数据输入空间上，建立不同运行性能等级的离线数据网络分类模型，实现“离线建模”。“在线评估”阶段，以数据滑动时间窗为评估单元，将过程性能分为稳定和过渡性能等级，把在线数据与历史性能等级进行相似度匹配。利用过程变量相对贡献度，对性能变化起决定性影响的过程变量进行识别和贡献度分析，为系统性能劣化原因的识别提供了参考。最后，应用到乙烯裂解过程在线性能评估中，说明了本评估方法可以对系统进行准确的在线性能评估。

Performance assessment method of chemical process based on multi-space total projection of latent structures

An online performance assessment method based on multi-space total projection of latent structures (MsT-PLS) was proposed for assessing operation status of chemical process by “offline modeling, online assessment” strategy. After the whole historical input data space was decomposed, interference information unrelated to quality variable in the input data space was eliminated by multi-space basis vector extraction method. The “off-line modeling”, an off-line data network classification model, with different operating performance grades was established in input multi-space related to the quality variable. During “online assessment”, assessment unit of data sliding time window was used to divide process performance into steady and transition performance grades and to match degree of similarity between online data and historical performance grades. According to relative contribution of all process variables, these process variables that decisively influence performance were identified and analyzed for degree of contribution, which provided a reference to identify causes for system degradation. Application to performance assessment of ethylene cracking process showed that the proposed assessment method can accurately assess system performance online.