The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

中文翻译：

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在全规模工厂场景中实施多块技术：在线预测不同丙烯腈丁二烯苯乙烯 (ABS) 产品连续工艺中的质量参数


该研究探讨了在全规模工厂场景中处理不同性质（过程和近红外传感器）的多块数据以进行在线质量预测的挑战，即以工业规模连续运行并生产不同等级丙烯腈丁二烯苯乙烯的工厂（ ABS）产品。此环境是评估多块数据分析方法使用的理想场景，可以增强数据解释、可视化和预测性能。特别是，作者提出了局部加权 PLS 的一种新颖的多块扩展，即局部加权多块偏最小二乘法 (LW-MB-PLS)。面向响应的顺序交替 (ROSA) 也已用于评估与聚合物相关的两个质量参数预测的不同块相关性。根据传感器类型和不同工厂部分将数据分为多个块，并通过增量添加数据块来构建不同的模型，以评估产品质量的早期估计是否可行。 ROSA 方法对两个质量参数都显示出有希望的预测性能，通过选择数据块突出显示最有影响力的植物部分。结果表明，早期和后期传感器在预测产品质量方面都发挥着至关重要的作用。实现了生产完成前对质量参数的合理估计。另一方面，所提出的 LW-MB-PLS 虽然在预测性能方面具有可比性，但可以减少特定产品的系统预测误差。这项研究为连续生产过程提供了宝贵的见解，为工厂操作员提供帮助，并为在线质量预测和控制的进步铺平了道路。 此外，它是作为 MB-PLS 的局部加权扩展来实现的。