This paper proposes a robust recursive principal component analysis (PCA) modeling procedure that aims to improve the monitoring performance by detecting and identifying process changes, removing disturbances, and updating the model to reflect the operating mode change. The proposed approach was applied to an industrial fired heater. Compared with previous approaches based on conventional PCA or recursive PCA, this new procedure demonstrated improved monitoring performance. The case study shows that both the number of false alarms and the number of model updates were significantly reduced in comparison with previous methods.

A new methodology is proposed to design a soft sensor for a polypropylene (PP) process with grade changeover operation. In contrast to the general polyolefin process, the PP process usually produces more than 100 different grades of products. Its reaction mechanism, based on seven catalysts, is so complex that neither mechanistic nor empirical models have been successful in describing full-scale industrial applications. The proposed methodology was developed based on the hybrid modeling of novel clustering and black-box and mechanistic models. Clustering based on critical to quality enables the soft sensor to handle the complexity of many different grades. Hybrid modeling offers good predictive power for transient behaviors as well as normal behaviors. The methodology also allows us to reduce the cost of building and updating the model. The developed soft sensor was successfully applied to a real industrial process. The accurate and reliable monitoring of the melt index in the PP process helped to significantly reduce the amount of off-specification product generation.

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In process monitoring that is based on statistical models, adaptive monitoring techniques have been developed to reflect frequent changes in the operating conditions. The key to adaptive monitoring of real industrial processes is to distinguish process operating condition changes from variations due to disturbances. This paper proposes a systematic method for detecting process state changes and classifying them as operating condition changes or variations as a result of disturbances. The key idea of the proposed method is to extract process knowledge that is based on if−then rules for detecting the operating condition changes. When a state change is accepted by a defined set of rules, it is classified as an operating mode change. Otherwise, it is classified as a disturbance. A signed digraph and statistical data analysis are used to generate the rules from the process knowledge. A robust cumulative-sum algorithm is used for change detection. The proposed method was validated using a dataset from an industrial process heater. The results showed a classification power of 97%. Adaptive monitoring that is based on the proposed method could significantly reduce the number of false alarms, compared to previous remodeling approaches.