CLEANROOM OPTIMISATION OF INLET-OUTLET CONFIGURATION AT VARIOUS ACH FOR PARTICLE DISPERSION IN ISO CLASS 5 SEMICONDUCTOR CLEANROOM STANDARD
DOI:
https://doi.org/10.35631/IJIREV.825015Keywords:
Airflow, ANOVA, CFD Analysis, Cleanroom, Optimization, Taguchi MethodAbstract
The performance of modular cleanrooms is highly dependent on effective airflow distribution and thermal control to minimize contamination and ensure product quality. This study investigates the influence of inlet–outlet configuration and operating parameters on airflow and temperature distribution inside a modular semiconductor cleanroom. Computational Fluid Dynamics (CFD) simulations were conducted to evaluate airflow vector patterns, mean air velocity, and temperature contours at different cross-sectional and working height levels. ANSYS Fluent was used for CFD simulation. Four key parameters, namely inlet size (number of FFUs), outlet size, inlet air temperature, and inlet air velocity, were optimized using the Taguchi method. Signal-to-noise (S/N) ratio analysis based on the larger-the-better criterion was employed to identify the optimal parameter levels for maximizing mean air velocity, while analysis of variance (ANOVA) was used to determine the contribution of each parameter. The results indicate that inlet air velocity is the most significant factor, contributing 56.16% to airflow performance, followed by inlet size, outlet size, and inlet air temperature. The optimal operating condition was identified as 6 FFU, a combined outlet configuration of two large outlets, size (1090 × 1096 mm) and one small outlet, size (534 × 1096 mm), an inlet air temperature of 294 K, and an inlet air velocity of 0.4 m/s. Under the optimal condition, the CFD predicted mean air velocity was 0.44 m/s, which showed good agreement with the experimental result of 0.45 m/s, with a deviation of approximately 2%. The optimized configuration demonstrates improved airflow uniformity and stable temperature distribution at the working height level, confirming the reliability of the proposed optimization framework. This study provides a practical framework for optimizing cleanroom airflow design by identifying the optimal combination of FFU number, inlet velocity, and outlet configuration to improve airflow uniformity, reduce contamination risk, and enhance energy efficiency in ISO Class 5 cleanrooms.
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References
Bhattacharya, A., Metcalf, A. R., Nafchi, A. M., & Mousavi, E. S. (2020). Particle dispersion in a cleanroom–effects of pressurization, door opening and traffic flow. Building Research & Information, 49, 294–307.
Chen, L., Wang, E., Li, Y., Weng, M., & Liu, F. (2022). Study on self-cleaning time and suspended particle distribution in medical clean room. Journal of Physics: Conference Series, 2148(1), 0012042.
Chen, Q. (2009). Ventilation performance prediction for buildings: A method overview and recent applications. Building and Environment, 44(4), 848–858.
Choi, J. I., & Edwards, J. R. (2012). Large-eddy simulation of human-induced contaminant transport in room compartments. Indoor Air, 22(1), 77–87.
Eslami, J., Abbassi, A., Saidi, M. H., & Bahrami, M. (2016). Effect of supply/exhaust diffuser configurations on contaminant distribution in ultra-clean environments. Energy and Buildings, 127, 648–657.
Indra Permana, & Wang, F. (2024). Performance improvement of a biotechnology vaccine cleanroom for contamination control. 82, 108248.
International Organization for Standardization. (2015). ISO 14644-1: Cleanroom and associated controlled environments—Part 1: Classification of air cleanliness. Geneva, Switzerland.
Loomans, M. G. L. C., Ludlage, T. B. J., van den Oever, H., Molenaar, P. C. A., Kort, H. S. M., & Joosten, P. H. J. (2020). Experimental investigation into cleanroom contamination build-up under reduced ventilation. Building and Environment, 176, 1–9.
MECART Cleanrooms. (2016, August 16). What is a cleanroom?
Michaleas, S. N., Laios, K., Charalabopoulos, A., Samonis, G., & Karamanou, M. (2021). Joseph Lister (1827–1912): A pioneer of antiseptic surgery. e32777.
Persily, A. K., & Emmerich, S. J. (2012). Ventilation rates in cleanrooms. ASHRAE Transactions, 118(1), 96–105.
Saidi, M. H., Sajadi, B., & Molaeimanesh, G. R. (2011). Effect of source motion on contaminant distribution in cleanrooms. Energy and Buildings, 43, 966–970.
Saidi, M. H., Sajadi, B., & Mozaffari, H. R. (2013). Airflow patterns and particle dispersion in cleanroom environments. Building and Environment, 63, 86–97.
Sandle, T. (2015). Pharmaceutical microbiology: Essentials for quality assurance and quality control. Woodhead Publishing.
Sandle, T. (2016). The development of cleanrooms: An historical review. Part 1.
Srebric, J., Vukovic, V., He, G., & Yang, X. (2008). CFD boundary conditions for contaminant dispersion and airflow. Building and Environment, 43, 294–303.
Strauss, L., Larkin, J., & Zhang, K. M. (2011). Occupancy as surrogate for particle concentrations. Energy and Buildings, 43(11).
Sun, Z., & Wang, S. (2010). CFD-based test method for indoor ventilation control. Building and Environment, 45, 1441–1447.
Tung, Y. C., Shih, Y. C., & Hu, S. C. (2009). Dispersion of airborne contaminants in isolation room. Applied Thermal Engineering, 29, 1544–1551.
Waring, M. S., & Siegel, J. A. (2008). Ventilation rate effect on particle removal. Indoor Air, 18(6), 509–520.
Whitfield, W. J. (1962). A new approach to cleanroom design. Sandia Corporation.
Yang, L., & Gan, C. E. (2007). Costing small cleanrooms. Building and Environment, 42, 743–751.
Yang, Z., Hao, Y., Shi, W., Shao, X., Dong, X., Cheng, X., Li, X., & Ma, X. (2021). Field test of pharmaceutical cleanroom cleanliness subject to multiple disturbance factors. Journal of Building Engineering, 42, 1–13.
Zhao, B., Wu, J., & Yang, X. (2004). Particle dispersion and deposition in indoor environments. Atmospheric Environment, 38(24), 4151–4162.
Zhou, L., Sun, W., Wu, C., Li, H., Zou, Z., & Huang, C. (2020). General particle concentration model and experimental validation for cleanrooms.
