Fuzzy logic-based improved ventilation system for the pharmaceutical industry

  • Authors

    • Sam Matiur Rahman Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
    • Mohammad Fazle Rabbi
    • Omar Altwijri
    • Mahdi Alqahtani
    • Tasriva Sikandar
    • Izzeldin Ibrahim Abdelaziz
    • Md. Asraf Ali
    • Kenneth Sundaraj
    2018-04-29
    https://doi.org/10.14419/ijet.v7i2.9985
  • Fuzzy Inference System, Air-Conditioning, Pharmaceutical Laboratories, Smart Ventilation.

  • Indoor air quality in pharmaceutical industry plays a vital role in the production and storing of medicine. Stable indoor environment including favorable temperature, humidity, air flow and number of microorganisms requires consistent monitoring. This paper aimed to develop a fuzzy logic-based intelligent ventilation system to control the indoor air quality in pharmaceutical sites. Specifically, in the proposed fuzzy inference system, the ventilation system can control the air flow and quality in accordance with the indoor temperature, humidity, air flow and microorganisms in the air. The MATLAB® fuzzy logic toolbox was used to simulate the performance of the fuzzy inference system. The results show that the efficiency of the system can be improved by manipulating the input-output parameters according to the user’s demands. Compared with conventional heating, ventilation and air-conditioning (HVAC) systems, the proposed ventilation system has the additional feature of the existence of microorganisms, which is a crucial criterion of indoor air quality in pharmaceutical laboratories.

  • References

    1. [1] Schneider RK (2001), Designing Clean Room HVAC Systems. ASHRAE journal. 43(8):39.

      [2] Yau YH, Chew BT, Saifullah AZ (2012), Studies on the indoor air quality of Pharmaceutical Laboratories in Malaysia. International Journal of Sustainable Built Environment. 1(1):110-24. https://doi.org/10.1016/j.ijsbe.2012.07.005.

      [3] Clement JD (1996). Reproductive health hazards in the pharmaceutical industry. Occupational medicine (Philadelphia, Pa.).12(1):131-43.

      [4] ANSI/ASHRAE Standard 55, (2004). Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta GA.

      [5] Becker K, Thull B, Käsmacher-Leidinger H, Stemmer J, Rau G, Kalff G, Zimmermann HJ (1997), Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model. Artificial intelligence in medicine. 11(1):33-53. https://doi.org/10.1016/S0933-3657(97)00020-1.

      [6] Kilic U, Gulluoglu MT, Guler H, Kaya T (2016), Analysis of Pulmonary and Hemodynamic Parameters for the Weaning Process by Using Fuzzy Logic Based Technology. International Conference on Information and Software Technologies 2016 Oct 13 (pp. 119-131). Springer International Publishing. https://doi.org/10.1007/978-3-319-46254-7_10.

      [7] Guler H, Kilic U, Kaya T (2016), The Design of Fuzzy Based Weaning Protocol in LabVIEW Environment. Proc. International Journal of Artificial Intelligence and Neural Networks. 6: 16 – 20.

      [8] Gates RS, Chao K, Sigrimis N (2001). Identifying design parameters for fuzzy control of staged ventilation control systems. Computers and Electronics in Agriculture. 31(1):61-74. https://doi.org/10.1016/S0168-1699(00)00174-5.

      [9] Villafáfila-Robles, R., & Salom, J (2012), Heat, Ventilation and Air Conditioning (HVAC). Electrical Energy Efficiency Technologies and Applications. 1: 335-355. https://doi.org/10.1002/9781119990048.ch11.

      [10] Villafáfilaâ€Robles R, Salom J. (2012), Heat, ventilation, and air conditioning (HVAC), A. Sumper, A. Baggini (Eds.), Electrical Energy Efficiency: Technologies and Applications, John Wiley and Sons, Chichester, UK, pp. 335-356. https://doi.org/10.1002/9781119990048.ch11.

      [11] Zhang L, Liu X, Jiang Y (2013). Application of entransy in the analysis of HVAC systems in buildings. Energy. 53:332-42. https://doi.org/10.1016/j.energy.2013.02.015.

      [12] Veleva V, Hart M, Greiner T, Crumbley C (2013), Indicators for measuring environmental sustainability: A case study of the pharmaceutical industry. Benchmarking an International Journal. 10(2):107-19.

      [13] Schneider JL, Wilson A, Rosenbeck JM (2010). Pharmaceutical companies and sustainability: an analysis of corporate reporting. Benchmarking: An International Journal.17 (3):421-34.

      [14] Avgelis A, Papadopoulos AM (2009). Application of multicriteria analysis in designing HVAC systems. Energy and Buildings. 41(7):774-80. https://doi.org/10.1016/j.enbuild.2009.02.011.

      [15] Ahamed NU, Taha ZB, Khairuddin IB, Rabbi MF, Rahaman SM, Sundaraj K (2012). Fuzzy logic controller design for intelligent air-conditioning system. In Control Science and Systems Engineering (ICCSSE), 2016 2nd International Conference on 2016 Jul 27 (pp. 232-236). IEEE.

      [16] Ahamed NU, Yusof Z, Hamedon Z, Rabbi MF, Sikandar T, Palaniappan R, Ali MA, Rahman SM, Sundaraj K (2016). Fuzzy logic controller design for intelligent drilling system. In Automatic Control and Intelligent Systems (I2CACIS), IEEE International Conference on 2016 Oct 22 (pp. 208-213). IEEE. https://doi.org/10.1109/I2CACIS.2016.7885316.

      [17] Xu M, Li S, Cai W (2005), Practical receding-horizon optimization control of the air handling unit in HVAC systems. Industrial & engineering chemistry research.44 (8):2848-55. https://doi.org/10.1021/ie0499411.

      [18] Jimenez L (2011), Molecular applications to pharmaceutical processes and cleanroom environments. PDA Journal of Pharmaceutical Science and Technology. 65(3):242-53. https://doi.org/10.5731/pdajpst.2011.00730.

      [19] Whyte W, Eaton T. A cleanroom contamination control system. European Journal of Parenteral and Pharmaceutical Sciences. 7(2):55-61.

      [20] Whyte W, Eaton T (2004), Microbial risk assessment in pharmaceutical cleanrooms. European Journal of Parenteral and Pharmaceutical Sciences. 9(1):16-23.

      [21] Ahamed, N. U., Benson, L., Clermont, C., Osis, S. T., & Ferber, R. (2017). Fuzzy Inference System-based Recognition of Slow, Medium and Fast Running Conditions using a Triaxial Accelerometer. Procedia Computer Science, 114, 401-407. https://doi.org/10.1016/j.procs.2017.09.054.

      [22] Ahamed, N. U., Taha, Z. B., Khairuddin, I. B. M., Rabbi, M. F., Sikandar, T., Palaniappan, R., Sundaraj, K. (2016, October). Development of fuzzy inference system for automatic tea making, IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 196-201).

  • Downloads

  • How to Cite

    Rahman, S. M., Rabbi, M. F., Altwijri, O., Alqahtani, M., Sikandar, T., Abdelaziz, I. I., Ali, M. A., & Sundaraj, K. (2018). Fuzzy logic-based improved ventilation system for the pharmaceutical industry. International Journal of Engineering & Technology, 7(2), 640-645. https://doi.org/10.14419/ijet.v7i2.9985