Prediction Models with Multiple Linear Regression for Improving Acoustic Performance of Textile Industry Plants
Abstract
In industrial plants noise is a major threat to the mental and physical health of employees. The risk increases more due to the presence of high noise sources and the presence of too many employees in textile industry plants. This paper aims to predict the consequences of variables that may arise in the plants for acoustic improvement in textile industry plants. For this purpose, scenario plants have been created according to architectural properties and source-transmission path-receiver characteristics. The acoustic analyses of the scenario plants were performed in the ODEON Auditorium, and A-weighted sound pressure level (LA), noise reduction (NR), and reverberation time (RT) were determined. From the data, prediction equations were created with a multiple linear regression (MLR) model. To test the prediction equations, acoustic measurements were made, and acoustics improvements were carried out at a textile industry plant located in Türkiye. When the obtained results, the success, validity, and reliability of the prediction method are provided. In conclusion, the effect of architectural properties and the surface absorption on acoustic improvements in the textile industry was revealed. It was emphasized that prediction methods can be used to determine the effectiveness of interventions that can be applied in different facilities and can be improved in future studies.Keywords:
industrial noise control, acoustics simulation, multiple linear regression, prediction methods, textile industry, ODEON Auditorium, noise reduction, reverberation timeReferences
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2. Al-Dosky B.H.M. (2014), Noise level and annoyance of industrial factories in Duhok City, IOSR Journal of Environmental Science, Toxicology and Food Technology, 8(5): 01–08, https://doi.org/10.9790/2402-08510108.
3. Ali S.A. (2011), Industrial noise level and annoyance in Egypt, Applied Acoustics, 72(4): 221–225, https://doi.org/10.1016/j.apacoust.2010.11.001.
4. Arenas J.P., Suter A.H. (2014), Comparison of occupational noise legislation in the Americas: An overview and analysis, Noise & Health, 14(72): 306–319, https://doi.org/10.4103/1463-1741.140511.
5. Atmaca E., Peker I., Altin A. (2005), Industrial noise and its effects on humans, Polish Journal of Environmental Studies, 14(6): 721–726.
6. Baffoe P.E., Duker A.A. (2018), Multiple linear regression approach to predicting noise pollution levels and their spatial patterns for the Tarkwa Mining Community of Ghana, American Journal of Engineering Research, 7(7): 104–112.
7. Baker D. (2015), Application of noise guidance to the assessment of industrial noise with character on residential dwellings in the UK, Applied Acoustics, 93: 88–96, https://doi.org/10.1016/j.apacoust.2015.01.018.
8. Bistafa S.R., Bradley J.S. (2000), Predicting reverberation times in a simulated classroom, Journal of the Acoustical Society of America, 108: 1721–1731, https://doi.org/10.1121/1.1310191.
9. Chatillon J. (2007), Influence of source directivity on noise levels in industrial halls: Simulation and experiments, Applied Acoustics, 68(6): 682–698, https://doi.org/10.1016/j.apacoust.2006.07.010.
10. Durán del Amor M.d.D., Caracena A.B., Llorens M., Esquembre F. (2022), Tools for evaluation and prediction of industrial noise sources. Application to a wastewater treatment plant, Journal of Environmental Management, 319: 115725, https://doi.org/10.1016/j.jenvman.2022.115725.
11. Ejigu M.A. (2019), Excessive sound noise risk assessment in textile mills of an Ethiopian-Kombolcha textile industry share company, International Journal of Research in Industrial Engineering, 8(2): 105–114, https://doi.org/10.22105/riej.2019.169138.1071.
12. Fichera I. (2020), The accuracy of reverberation time prediction for general teaching spaces, [in:] INTERNOISE and NOISE-CON Congress and Conference Proceedings, pp. 1738–1748.
13. Fredriksson S., Hammar O., Torén K., Tenenbaum A., Waye K.P. (2015), The effect of occupational noise exposure on tinnitus and sound-induced auditory fatigue among obstetrics personnel: A cross-sectional study, BMJ Open, 5(3): e005793, https://doi.org/10.1136/bmjopen-2014-005793.
14. Ilgürel N. (2013), Effectiveness of the total absorption on noise reduction in industrial plants, Noise Control Engineering Journal, 61(1): 11–25, https://doi.org/10.3397/1.3702002.
15. Jayawardana T.S.S., Perera M.Y.A., Wijesena G.H.D. (2014), Analysis and control of noise in a textile factory, International Journal of Scientific and Research Publications, 4(12).
16. Job R.F.S. (1996), The influence of subjective reactions to noise on health effects of the noise, Environment International, 22(1): 93–104, https://doi.org/10.1016/0160-4120(95)00107-7.
17. Kumar V., Kumar S. (2016), A regression model of traffic noise intensity in metropolitan city using artificial neural networks, International Journal of Research and Engineering, 3(12): 27–30.
18. Kurra S. (2020), Environmental Noise and Management: Overview From Past to Present, Hoboken, NJ: John Wiley & Sons, Ltd.
19. Leather P., Beale D., Sullivan L. (2003), Noise, psychosocial stress and their interaction in the workplace, Journal of Environmental Psychology, 23(2): 213–222, https://doi.org/10.1016/S0272-4944(02)00082-8.
20. Masullo M., Toma R.A., Maffei L. (2022), Effects of industrial noise on physiological responses, Acoustics, 4: 733–745, https://doi.org/10.3390/acoustics4030044.
21. McIntosh A.M., Sharpe M., Lawrie S.M. (2010), Research methods, statistics and evidence-based practice, [in:] Companion to Psychiatric Studies, Johnstone E.C., Owens Cunningham D., Lawrie S.M., McIntosh A.M., Sharpe M.D. [Eds.], 8th ed., pp. 157–198, Elsevier Churchill Livingstone.
22. Monazzam M.R., Nezafat A. (2007), On the application of partial barriers for spinning machine noise control: A theoretical and experimental model, Iranian Journal of Environmental Health Science and Engineering, 4(2): 113–120.
23. Monazzam-Esmaeelpour M.R., Hashemi Z., Golmohammadi R., Zaredar N. (2014), A passive noise control approach utilizing air gaps with fibrous materials in the textile industry, Journal of Research in Health Sciences, 14(1): 46–51.
24. Nowoświat A. (2023), Determination of the reverberation time using the measurement of sound decay curves, Applied Sciences, 13: 8607, https://doi.org/10.3390/app13158607.
25. Occupational Safety and Health Administration (1995), 1910.95 – Occupational noise exposure, https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.95.
26. Probst F. (2012), Prediction of sound pressure levels at workplaces, [in:] Acoustics 2012, pp. 23–27.
27. Reinhold K., Tint P. (2009), Hazard profile in manufacturing: determination of risk levels towards enhancing the workplace safety, Journal of Environmental Engineering and Landscape Management, 17(22): 69–80, https://doi.org/10.3846/1648-6897.2009.17.69-80.
28. Shahid A., Jamali T., Kadir M.M. (2018), Noise induced hearing loss among an occupational group of textile employees in Karachi, Pakistan, Occupational Medicine & Health Affairs, 6(4): 282, https://doi.org/10.4172/2329-6879.1000282.
29. Shakhatreh F.M., Abdul-Baqi K.J., Turk M.M. (2000), Hearing loss in a textile factory, Saudi Medical Journal, 21: 58–60.
30. Tang X., Kong D., Yan X. (2018), Multiple regression analysis of a woven fabric sound absorber, Textile Research Journal, 89(5): 855–866, https://doi.org/10.1177/0040517518758001.
31. The European Parliament and the Council of the European Union (2003), Directive 2003/10/EC of the European Parliament and of the Council of 6 February 2003 on the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents (noise), Official Journal of the European Union.
32. Themann C.L., Masterson E.A. (2019), Occupational Noise exposure: A review of its effects, epidemiology, and impact with recommendations for reducing its burden, Journal of the Acoustical Society of America, 146(5): 3879–3905, https://doi.org/10.1121/1.5134465.
33. Yahya M.N., Otsuru T., Tomiku R., Okozono T. (2010), Investigation the capability of neural network in predicting reverberation time on classroom, International Journal of Sustainable Construction Engineering and Technology, 1(1): 1–13.
34. Yaman Turan N., Oney O. (2021), Investigation of the noise exposure in weaving workplaces in Western Turkey, Textile and Apparel, 31(1): 27–33, https://doi.org/10.32710/tekstilvekonfeksiyon.762195.
35. Yang M. (2019), A review of regression analysis methods: Establishing the quantitative relationships between subjective soundscape assessment and multiple factors, [in:] Proceedings of the 23rd International Congress on Acoustics, https://doi.org/10.18154/RWTHCONV-239497.
36. Zaw A.K. et al. (2020), Assessment of noise exposure and hearing loss among employees in textile mill (Thamine), Myanmar: A cross-sectional study, Safety and Health at Work, 11(2): 199–206, https://doi.org/10.1016/j.shaw.2020.04.002.
