在全面工厂场景中实施多块技术:在连续过程中对不同烯丁乙烯 (ABS) 产品的质量参数进行在线预测
Daniele Tanzilli1, Lorenzo Strani2, Francesco Bonacini3
1Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy; Centre National de la Recherche Scientifique (CNRS), Laboratoire de Spectroscopie pour les Interactions, la Réactivitè et l'Environnement (LASIRE), Cité Scientifique, University Lille, F-59000, Lille, France.
Analytica chimica acta
|July 5, 2024
概括
本研究介绍了局部加权的多块部分最小方程 (LW-MB-PLS),用于在连续ABS生产中增强在线质量预测. 这些方法可以提前评估质量,改善过程控制和减少错误.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
- 聚合物科学 聚合物科学
背景情况:
- 处理多种多块数据 (过程,NIR传感器) 的挑战,以实时预测工业ABS生产的质量.
- 需要先进的多块数据分析来改善数据解释,可视化和预测准确性.
- 评估连续工业流程,以评估多块数据分析方法.
研究的目的:
- 提出和评估一种新的局部加权部分最小平方 (LW-MB-PLS) 的多块扩展.
- 评估在连续ABS制造中早期在线质量估计的可行性.
- 确定不同数据块 (传感器类型,工厂部分) 对质量预测的相关性.
主要方法:
- 开发局部加权多块部分最小平方 (LW-MB-PLS).
- 应用响应导向序列交替 (ROSA) 来评估区块相关性.
- 增量添加数据块 (按传感器类型和工厂部分) 来构建预测模型.
主要成果:
- 罗萨展示了强大的预测性能,识别了有影响力的工厂部分.
- 早期和后期阶段的传感器都被发现对质量预测至关重要.
- 对于特定的ABS等级,LW-MB-PLS实现了可比的预测性能,同时减少了系统错误.
- 在完成最终生产阶段之前,可以对质量参数进行可行的早期估计.
结论:
- 该研究为优化连续生产流程提供了重要的见解.
- 开发的LW-MB-PLS方法为ABS制造提供了在线质量预测和控制方面的进步.
- 结果支持工厂运营商实时决策和流程改进.
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