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.
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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 |
|---|---|
| Original Title: | Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries |
| English Title: | Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries |
| 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 |
| Item ID: | 94058 |
| Depositing User: | Dr Sin Yong Teng |
| Date Deposited: | 23 May 2019 09:28 |
| Last Modified: | 29 Sep 2019 22:48 |
| References: | Abdi, H. and Williams, L. J. (2010). Principal Component Analysis. John Wiley &Sons, Inc.,2, 433-459. Aida, S., Matsuno, T., Hasegawa, T., Tsuji, K. (2017). Application of principal component analysis for improvement of X-ray fluorescence images obtained by polycapillary-based micro-XRF technique. Nuclear Instruments and Methods inPhysics Research B, 402, 267-273. Al-Sayed, A. (2015). Principal component analysis within nuclear structure. Nuclear Physics A, 933, 154-164. Alsayigh, A. (2015), Applying Lean Principles to Transform Conventional Oil & Gas Amminudin, K., Enezi, T., Jubran, M., Hajji, A.,Bedoukhi, Z. (2011). Gas plant improves C3 recovery with Lean Six Sigma approach. 19th Annual Technical Conference of the GCC Chapter of the Gas Processors Association. Anderson, M. (2018). Design Expert Documentation. [online] Statease. Available at: https://www.statease.com/docs/v11/tutorials/combinedmix-process.html [Accessed 29 Oct. 2018]. Aspen Process Engineering Webinar (2006). Aspen HYSYS Property Packages Overview and Best Practices for Optimum Simulations. Aspentech, Galaxis, Singapore. Atanas, J., Rodrigues, C., Simmons, R. (2016). Lean Six Sigma Applications in Oil and Gas Industry: Case Studies. International Journal of Scientific and Research Publications, 6(5), 540-544. Bhattacharya, A., Jain, R., Choudhary, A. (2011). Green Manufacturing Energy, Products and Processes. The Boston Consulting Group, 1-24. Box, G. E. P., Behnken, D. W. (1960). Some New Three Level Designs for the Study of Quantitative Variables. Technometrics, 2(4), 455-475. Bratchell, N. (1989). Multivariate response surface modelling by principal components analysis. Journal of Chemometrics, 3(4), 579-588. MANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Ning, C. and You, F. (2018). Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods. Computers and Chemical Engineering, Volume 112, 190-210. Collins, L., Dziak, J., Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods, 14(3), 202-224. Čuček, L., Klemeš, J.,Kravanja, Z. (2012). A Review of Footprint analysis tools for monitoring impacts on sustainability. Journal of Cleaner Production, 34, 9-20. Chiang, L., Lu, B., Castillo, I. (2017). Big data analytics in chemical engineering. Annual review of chemical and biomolecular engineering, 8, 63-85. Derringer, G. and Suich, R. (1980). Simultaneous Optimization of Several Response Variables.Journal of Quality Technology, 12(4), 214219. Drovandi, C.C., Holmes, C., McGree, J.M., Mengersen, K., Richardson, S., Ryan, E.G. (2017). Principles of experimental design for Big Data analysis. Statistical science: a review journal of the Institute of Mathematical Statistics, 32(3), p.385. Fisher, R. A. (1935). The Design of Experiments. Edinburg: Oliver & Boyd. Gunst, R. F. and Mason, R. L. (2009). Fractional factorial design. WIREs CompStat,1, 234-244. Halvorsen, M., Elseth, G.,Naevdal, O. (2012). Increased oil production at Troll by autonomous inflow control with RCP valves. SPE Annual Technical Conference and Exhibition. Hostelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417441. How, B.S. and Lam, H.S. (2018a). Sustainability evaluation for biomass supply chain synthesis: Novel principal component analysis (PCA) aided optimisation approach. Journal of Cleaner Production, 189, 941-961. How, B.S. and Lam, H.S. (2018b). PCA Method for Debottlenecking of Sustainability Performance in Integrated Biomass Supply Chain. (In Press) MANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Process Integration and Optimization for Sustainability, doi.org/10.1007/s41660-018-0036-3. Hu, Y., Li, G., Chen, H., Li, H., Liu, J. (2016). Sensitivity analysis for PCAbased chiller sensor fault detection. International Journal of Refrigeration, 63, 133-143. Huda, L. and Matondang, R. (2018). The lean ergonomics in green design of crude palm oil plant. IOP Conference Series: Materials Science and Engineering, 309, 012109. International Organization for Standardization (ISO) (2006). ISO 14044 :Environmental management - Life cycle assessment - Requirements and guidelines. 1st ed. Geneva: ISO. Jackson, D. (1993). Stopping Rules in Principal Components Analysis: A Comparison of Heuristical and Statistical Approaches.Ecology, 74(8), pp.2204-2214. Jolliffe, I. and Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. Joly, M., Moro, L., Pinto, J. (2002). Planning and scheduling for petroleum refineries using mathematical programming. Brazilian Journal of Chemical Engineering, 19(2), 207-228. Klemeš, J. and Kravanja, Z. (2013). Forty years of Heat Integration: Pinch Analysis (PA) and Mathematical Programming (MP). Current Opinion in Chemical Engineering, 2(4), 461-474. Klemeš, J., Varbanov, P.,Huisingh, D. (2012). Recent cleaner production advances in process monitoring and optimisation. Journal of Cleaner Production, 34, 1-8. Klemeš, J., Dhole, V., Raissi, K., Perry, S.,Puigjaner, L. (1997). Targeting and design methodology for reduction of fuel, power and CO2 on total sites. Applied Thermal Engineering, 17(8-10), 9931003. Kraber, S. (2009). Response Surface Optimization. Stat-Ease Inc. Krishna Madhavi, S., Sreeramulu, D., Venkatesh, M. (2017). Evaluation of Optimum Turning Process of Process Parameters Using DOE and PCA Taguchi Method. Materials Today: Proceedings, 4(2), 1937-1946. MANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Lam, H., How, B. and Hong, B. (2015). Green supply chain toward sustainable industry development. Assessing and Measuring Environmental Impact and Sustainability, 409-449. Leong. W.D., Lam. H.L., Ng. W.P.Q., Lim. C.H., Tan. C.P., Ponnabalam. S.G., (2018), Lean and Green Manufacturing - A Review on Application and Effect. Process Integration and Optimization for Sustainability. (In Press) McKay, B., Willis, M., Barton, G. (1997). Steady-state modelling of chemical process systems using genetic programming. Computers & Chemical Engineering, 21(9), 981-996. Morita, T., Yogo, S., Koike, M., Hamaguchi, T., Jung, S., Koshijima, I., Hashimoto, Y., (2013). Detection of cyber-attacks with zone dividing and PCA. Procedia Computer Science, 22, 727-736. Murray, P. and Forfar, L. (2017). The Application of Advanced Design of Experiments for the Efficient Development of Chemical Processes. Chemical Informatics, 03(02). Nobi, A., Alam, S., Lee, J.W., (2017). Dynamic of consumer groups and response of commodity markets by principal component analysis. Physica A: Statistical Mechanics and its Applications, 482, 337-344. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine,2,559-572. http://pbil.univlyon1.fr/R/pearson1901.pdf Porter, M. E. and Linde, V. D. (1995). Green and competitive: Ending the stalemate. Harvard Bus. Rev., 73(5), 120-134. Production Operations in a Gulf State into Cleaner Energy, PhD Thesis, Loughborough University, England. Rea, A. and Rea, W., (2016). How Many Components should be Retained from a Multivariate Time Series PCA? arXiv preprint arXiv:1610.03588. Schneider, D. F. (1997). Debottlenecking Options and Optimization, Houston: Stratus Engineering, Inc. Shlens, J. (2014). A Tutorial on Principal Component Analysis. eprintarXiv:1404.1100. MANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Smith, R. and Linnhoff, B. (1988). Design of separators in the context of overall processes. Chemical Engineering Research and Design, 66(3), 195-228. Telford, J. K. (2007). A brief introduction to design of experiments. JohnsHopkins APL Technical Digest (Applied Physics Laboratory), 27(3), 224-232. The Carbon Trust (2011). Energy and Carbon conversion factors, London: The Carbon Trust. Toyota, H., Asai, T. and Oku, N. (2017). Process optimization by use of design of experiments: Application for liposomalization of FK506. European Journal of pharmaceutical Sciences, 102, 196-202. Wan Alwi, S.R., Abdul Manan, Z., Klemeš, J.J., Huisingh, D., (2014). Sustainability engineering for the future. Journal of Cleaner Production, 71, 1-10. Wang, X., Jiang, D. and Lang, X. (2017). Future extreme climate changes linked to global warming intensity. Science Bulletin, 62(24), 1673-1680. WBCSD Chemicals (2013). Life Cycle Metrics for Chemical Products: A guideline by the chemical sector to assess and report on the environmental footprint of products, based on life cycle assessment, Washington: WBCSD Chemicals. WCED (1987). The Brundtland Report: 'Our common future', Oxford: Oxford University Press. Zhang, Y. and Edgar, T. (2008). PCA Combined Model-Based Design of Experiments (DOE) Criteria for Differential and Algebraic System Parameter Estimation. Industrial & Engineering Chemistry Research, 47(20), 7772-7783. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/94058 |
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