Principal Component Analysis-aided Statistical Process Optimization
Principal Component Analysis-aided Statistical Process Optimization (PASPO)
Principal Component Analysis-aided Statistical Process Optimization (PASPO) is a method of industrial processing systems optimization by an advanced statistical approach [1]. The industrial data processing method uses a correlation-based Principal Component Analysis for simultaneous dimension reduction and processing variable selection. This effectively finds and targets critical operating variables in large-scale industrial systems such as refineries, processing plants, factories and manufacturing facilities. The targeted variables are then optimized by multiple surface response methodologies which is suitable for systems that contain minor noises. The PASPO method has been verified in industrial refineries to provide robust, reliable and effective results given multiple attributed objectives. The method is capable of utilizing big data that are logged in process information management system for data-driven statistical optimization.
The PASPO classifies under statistical process optimization, which is a sub-study of Statistical Process Control (also known as SPC) for continuous process improvements. Statistical optimization methods are not common in Industrial Engineering, as they are difficult to implement with too many processing equipment, causing high dimensional problems. Traditionally, industrial optimization methods are either graph-based[2] , model-based[3] or simulation-based. Statistical methods are known to be reliable but are only used for incremental improvements [4] and not facility-wide optimization. However, with the use of Principal Component Analysis, the statistical methods have shown to be able to carry out facility-wide optimization with highly practical implementations.
- ↑ Teng, Sin Yong; How, Bing Shen; Leong, Wei Dong; Teoh, Jun Hao; Adrian, Chee Siang Cheah; Motavasel, Zahra; Lam, Hon Loong (2019). "Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries". Journal of Cleaner Production. doi:10.1016/j.jclepro.2019.03.272.
- ↑ Hon Loong, Lam (2013). "Extended P-graph applications in supply chain and Process Network Synthesis". Current Opinion in Chemical Engineering. 2 (4): 475–486. doi:10.1016/j.coche.2013.10.002. Retrieved 26 March 2019.
- ↑ Kasivisvanathan, Harresh; Ng, Rex T.L.; Tay, Douglas H.S.; Ng, Denny K.S. (2012). "Fuzzy optimisation for retrofitting a palm oil mill into a sustainable palm oil-based integrated biorefinery". Chemical Engineering Journal. 200-202: 694–709. doi:10.1016/j.cej.2012.05.113. Retrieved 26 March 2019.
- ↑ S Oakland, John (2007). "Statistical Process Control": 23. doi:10.4324/9780080551739. Retrieved 26 March 2019.
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