Operating performance assessment (OPA) of process industries plays a significant role in improving the product quality and pursuing the best economic benefits. With the continuous improvement of industrial intelligence, massive data has been collected. Variable time-delay issue is brought by the different spatial and temporal distributions of production units, which affects the actual causal relationship of collected data and assessment accuracy. Therefore, a spatiotemporal synergetic operating performance assessment (SSOPA) method is proposed in this paper. First, a reconstructed correlation matrix is carefully crafted in both time and space, leveraging the intricate multi-correlated spatiotemporal-delay (MCSD) parameters. To precisely quantify the multi-correlated relationships, a mixed gray relation analysis (MGRA) is employed. Subsequently, a predatory search-based genetic algorithm (PSGA) is utilized to meticulously search for the optimal MCSD parameters. Once these parameters are determined, an OPA model is formulated for each subsystem, drawing upon aligned process data and a distributed dissimilarity analysis (DISSIM). Finally, based on the subsystem-level OPA, a comprehensive assessment of the global operating performance level is conducted. The feasibility and effectiveness of the proposed method are verified by the plant-wide hot strip mill process (PHSMP).

中文翻译：

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基于重建相关矩阵和全厂热轧工艺相异分析的时空协同运行绩效评估

过程工业的运行绩效评估（OPA）对于提高产品质量、追求最佳经济效益具有重要作用。随着工业智能化的不断提升，海量数据被收集。生产单元时空分布的不同带来了可变时滞问题，影响了采集数据的实际因果关系和评估准确性。因此，本文提出一种时空协同运营绩效评估（SSOPA）方法。首先，利用复杂的多相关时空延迟（MCSD）参数，在时间和空间上精心构建重建的相关矩阵。为了精确量化多重相关关系，采用了混合灰色关系分析（MGRA）。随后，利用基于掠夺性搜索的遗传算法（PSGA）仔细搜索最佳 MCSD 参数。一旦确定了这些参数，就会利用对齐的过程数据和分布式相异性分析 (DISSIM)，为每个子系统制定 OPA 模型。最后，基于子系统级OPA，对全局运行性能水平进行综合评估。通过全厂热连轧工艺（PHSMP）验证了该方法的可行性和有效性。