Optimization of Pneumatic Conveying System at Ras Lanuf Polyethylene Plant to Reduce Blockages and Production Loss
DOI:
https://doi.org/10.5281/zenodo.21706448Keywords:
Pneumatic conveying, polyethylene plant, angel hair formation, pipe erosion, pressure drop, Ras LanufAbstract
The Ras Lanuf Polyethylene Plant in Libya, utilizing Dow UNIPOL™ gas-phase technology with a combined capacity of 160,000 tonnes/year, has experienced chronic pneumatic conveying failures following operational restart, resulting in 2,839 downtime hours, 637 blockage incidents, 15,200 tonnes of lost production, and 450 tones of annual material loss. This mixed-methods study integrates quantitative operator surveys (n=32), qualitative technical lead interviews (n=5), multi-year operational data (November 2019–February 2026), and fluid mechanics literature to optimize the conveying system. Findings reveal three interconnected failure mechanisms: low conveying velocity (v < 20 m/s) and pipe roughness cause gravitational settling and blockages; high velocities (v > 30 m/s) exponentially accelerate angel hair formation (2.7-fold increase) and pipe erosion (E ∝ v²·⁵); while pre-blockage pressure drop variability (CV > 0.20) predicts blockages within 4 hours with 73% probability. The proposed integrated optimization strategy—comprising section-specific velocity targeting (22–28 m/s), automated SCADA pressure monitoring, bypass air injection at transitions, soft fin inserts, and ceramic-lined bends—is projected to reduce blockages by 50–60%, downtime by 40–50%, recover 160–180 tonnes of resin annually, and restore 2,450–3,266 tonnes of annual production capacity. This research provides the first field-validated empirical dataset from a commercial gas-phase polyolefin plant following extended shutdown, offering petrochemical managers an evidence-based operational roadmap to eliminate unscheduled downtime and ensure manufacturing excellence.
References
Behera, N., Agarwal, V. K., Jones, M. G., & Williams, K. C. (2022). Fluidized dense phase pneumatic conveying: A review. Particulate Science and Technology, 40(7), 811–835. https://doi.org/10.1080/02726351.2022.2080619
Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139–1160.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Cai, J., Qi, H., Xu, P., & Yao, X. (2014). Comparison of pressure drops through different bends in dense-phase pneumatic conveying system at high pressure. Experimental Thermal and Fluid Science, 54, 120–128. https://doi.org/10.1016/j.expthermflusci.2014.03.016
Cengel, Y. A., & Boles, M. A. (2014). Thermodynamics: An engineering approach (8th ed.). McGraw-Hill Education.
Chandra, P., Gopalakrishnan, N., & Deen, N. G. (2018). Prediction of dilute phase pneumatic conveying characteristics using MP-PIC method. In Proceedings of the 12th International Conference on CFD in Oil & Gas, Metallurgical and Process Industries (pp. 639–646).
Chen, G. (2014). Experimental and theoretical advances for innovative bypass pneumatic conveying system design [Doctoral dissertation, University of Wollongong]. University of Wollongong Research Online.
Chen, X., Wang, S., Ding, S., Zhao, Y., & Xiao, Y. (2025). Insight into the mesoscale slug characteristics in the dense horizontal pneumatic conveying process. Industrial & Engineering Chemistry Research, 64(3), 1580–1593. https://doi.org/10.1021/acs.iecr.4c03759
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Fowler, F. J. (2014). Survey research methods (5th ed.). SAGE Publications.
Hadžiahmetović, N., Hodžić, N., Kahrimanović, D., & Džaferović, E. (2014). Predicted elbow erosion model based on computational fluid dynamics. Tehnicki Vjesnik – Technical Gazette, 21(2), 275–282.
Hu, X., Wang, J., Li, F., & Sun, D. (2023). Structural optimization of high-pressure polyethylene cyclone separator based on energy efficiency parameters. Processes, 11(3), Article 691. https://doi.org/10.3390/pr11030691
Kaur, B., Mittal, A., Wypych, P. W., & Mallick, S. S. (2017). Numerical simulation of fluidized dense-phase pneumatic conveying of powders to develop improved model for solids friction factor. Particuology, 34, 80–89. https://doi.org/10.1016/j.partic.2016.11.006
Keith, T. Z. (2019). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (3rd ed.). Routledge.
Kuang, S., Zhou, M., & Yu, A. (2020). CFD-DEM simulation of large-scale dilute-phase pneumatic conveying system. Industrial & Engineering Chemistry Research, 59(10), 4150–4160. https://doi.org/10.1021/acs.iecr.9b03008
Lavrakas, P. J. (2008). Encyclopedia of survey research methods. SAGE Publications. https://dx.doi.org/10.4135/9781412963947
Mills, D. (2016). Pneumatic conveying design guide (3rd ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-08-100649-8.00003-2
Müller, A., & Isermann, R. (2008). Condition monitoring of HVAC systems. Control Engineering Practice, 16(9), 1089–1100.
Nan, G. (2025). AI control techniques for stabilization and energy-saving of pneumatic conveying systems [Doctoral dissertation, University of Wollongong]. University of Wollongong Research Online. https://doi.org/10.71747/uow-r3gk326m.28934858
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147
Resnik, D. B. (2020). The ethics of research with human subjects: Protecting people, advancing science, promoting trust. Springer.
Rinoshika, A., Yan, Y., & Zhang, J. (2010). An experimental study of energy-saving pneumatic conveying system in a horizontal pipeline with dune model. Powder Technology, 198(1), 49–55. https://doi.org/10.1016/j.powtec.2009.10.013
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). John Wiley & Sons.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson Education.
Taherdoost, M. (2016). Sampling methods in research methodology: How to choose a sampling technique for research. International Journal of Academic Research in Management, 5(2), 18–27.
Tashakkori, A., & Teddlie, C. (2010). SAGE handbook of mixed methods in social & behavioral research (2nd ed.). SAGE Publications.
Uzi, A., Levy, A., & Gat, S. (2017). Erosion prediction of industrial conveying pipelines. Powder Technology, 309, 49–60. https://doi.org/10.1016/j.powtec.2016.12.087
Wang, X., Zhang, Y., Liu, H., & Li, Z. (2023). Study on the influence of different factors on pneumatic conveying in horizontal pipe. Applied Sciences, 13(9), Article 5483. https://doi.org/10.3390/app13095483
Wiwattanasirikul, T., Sirisopapong, P., & Khongthong, P. (2011). The flow conditions to move different plastic particles in dilute pneumatic conveying. Applied Mechanics and Materials, 148–149, 932–936. https://doi.org/10.4028/www.scientific.net/AMM.148-149.932
Yadav, R., et al. (2024). CFD modeling of swirling flow mechanisms to reduce elbow erosion in pneumatic conveying. Powder Technology, 428, Article 119210.
Yan, Y., Rinoshika, A., & Zhang, J. (2012). Pressure drop reduction in horizontal pneumatic conveying using soft fin inserts. Chemical Engineering Science, 75, 112–120.
Zheng, Y. (2024). CFD-DEM investigation of wear characteristics in horizontal-to-vertical pipe bends during dilute-phase pneumatic conveying. Wear, 538, Article 205210.
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