Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Teng, Sin Yong and How, Bing Shen and Leong, Wei Dong and Teoh, Jun Hao and Cheah, Adrian Chee Siang and Motavasel, Zahra and Lam, Hon Loong (2019): Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries. Published in: Journal of Cleaner Production , Vol. 225, (10 July 2019): pp. 359-375.

Abstract

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively.

• Item Type: MPRA Paper
• Language: English
• Keywords: Principal Component Analysis, Design of Experiment, Plant-wide Optimisation, Statistical Process Optimization, PASPO, Big Data Analytics
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; C - Mathematical and Quantitative Methods > C9 - Design of Experiments; L - Industrial Organization > L6 - Industry Studies: Manufacturing

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