Multiscale Framework for Real-Time Process Monitoring of Nonlinear Chemical Process Systems,Industrial & Engineering Chemistry Research

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Multiscale Framework for Real-Time Process Monitoring of Nonlinear Chemical Process Systems
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2020-09-18 , DOI: 10.1021/acs.iecr.0c02288
Muhammad Nawaz ₁ , Abdulhalim Shah Maulud _{1,

2} , Haslinda Zabiri ₁ , Humbul Suleman ₃ , Lemma Dendena Tufa ₄

Affiliation

Process monitoring techniques are used in the chemical industry to improve both product quality and plant safety. In chemical process systems, real-time process monitoring is one of the most crucial and challenging tasks for efficient quality control of the final products and process optimization. The existing multiscale process monitoring techniques use offline decomposition tools that restrict their applications to real-time process monitoring. In this study, to improve the performance of monitoring real-time process data, we have combined moving window-based wavelet transform and kernel principal component analysis (KPCA). A case study is performed on a typical continuous stirred tank reactor system. Performance analysis (based on T² and squared prediction error statistics and contribution plots) shows that the technique successfully detects and identifies process disturbances, sensor bias, and process faults. Moreover, a comparison with PCA and KPCA methods shows that the proposed approach provides a 100% fault detection rate for the step-change fault patterns and has considerably improved detection rates for the random and ramp-change fault patterns.

中文翻译：

非线性化学过程系统实时过程监控的多尺度框架

化学工业中使用过程监控技术来提高产品质量和工厂安全性。在化学过程系统中，实时过程监控是对最终产品进行有效质量控制和过程优化的最关键和最具挑战性的任务之一。现有的多尺度过程监视技术使用离线分解工具，这些工具将其应用程序限制为实时过程监视。在这项研究中，为了提高监视实时过程数据的性能，我们将基于移动窗口的小波变换与内核主成分分析（KPCA）相结合。在典型的连续搅拌釜反应器系统上进行了案例研究。性能分析（基于T ²预测误差统计和贡献图的平方）表明该技术成功地检测并识别了过程干扰，传感器偏差和过程故障。此外，与PCA和KPCA方法的比较表明，所提出的方法为逐步变化的故障模式提供了100％的故障检测率，并为随机和斜坡变化的故障模式提供了显着提高的检测率。

更新日期：2020-10-15

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