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Neural Fuzzy Control of the Indoor Air Quality Onboard a RO–RO Ship Garage

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Abstract

Air quality control is necessary to improve the environmental condition in particular Indoor Air Quality (IAQ), which has a direct impact on a living organism. In marine application, the International Maritime Organization (IMO) made standards to determine the gas emission limits and specify the air circulation in IAQ in addition to employing in energy-efficient and energy management system which leads to reduce emission and enhance the environmental condition by efficient and economical way. Applying advanced energy management policies and control could be achieved by energy consumption. In this paper, an intelligent neuro-fuzzy controller has been designed to model and control carbon monoxide (CO) concentration for a real case study of a high-speed craft passenger ship with a vehicle garage onboard for a liner between Egypt and Saudi Arabia ports. The relation between emitted CO and the number of cars and their positions has been modelled using artificial neural network (ANN). The ANN model has been built and validated based on real measurements of CO at different ventilation conditions of the case study. Different fuzzy controllers, fixed and adaptive, are designed to control CO during loading and unloading states. Scaling factors of fuzzy controller are adapted using two different ways namely Supervisor Fuzzy Controller (SFC) and Particle Swamp Optimization (PSO). Simulation results have analysed the proposed control system at different conditions. The obtained outcomes manifest the fact that the controller tends to work robustly and efficiently to maintain CO at the permissible allowed range.

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References

  1. IMO: Development of amendments to SOLAS regulation II-2/20 and associated guidance on air quality management for ventilation of closed vehicle spaces, closed RO–RO and special category spaces. IMO—Maritime Safety Committee 1st session—Agenda item 6, 3 (2013)

  2. EPA, U.S. Environmental Protection Agency: An introduction to indoor air quality, carbon monoxide (2011)

  3. Occupational Safety and Health Administration (OSHA): Roll-on roll-off (RO–RO) ship and dock safety. U.S. Department of Labor, 3396-06N (2010)

  4. U.S. Environmental Protection Agency: Indoor air pollution: an introduction for health professionals. The American Lung Association (ALA), The Environmental Protection Agency(EPA), The Consumer Product Safety Commission (CPSC), and The American Medical Association (AMA). U.S. Government Printing Office Publication No. 1934-523-217/81322, EPA 402-R-94-007 (1994)

  5. Krarti, M., Ayari, A: Ventilation for enclosed parking garages. ASHRAE J. 52–55 (2001). http://www.ashraejournal.org

  6. Chaloulakou, A., Duci, A., Spyrellis, N.: Exposure to carbon monoxide in enclosed multi-level parking garages in the central Athens urban area. Indoor Built Environ. 11, 191–201 (2002)

    Article  Google Scholar 

  7. Papakonstantinou, K., Chaloulakou, A., Duci, A., Vlachakis, N., Markatos, N.: Air quality in an underground garage: computational and experimental investigation of ventilation effectiveness. Energy Build. 35(9), 933–940 (2003)

    Article  Google Scholar 

  8. Duci, A., Papakonstantinou, K., Chaloulakou, A., Markatos, N.: Numerical approach of carbon monoxide concentration dispersion in an enclosed garage. Build. Environ. 39, 1043–1048 (2004)

    Article  Google Scholar 

  9. Kim, S.S., Lee, Y.G.: Field measurements of indoor air pollutant concentrations on two new ships. Build. Environ. 45, 2141–2147 (2010)

    Article  Google Scholar 

  10. Song, Y., Wu, S., Yan, Y.Y.: Control strategies for indoor environment quality and energy efficiency—a review. Int. J. Low-Carbon Technol. 10, 305–312 (2015)

    Article  Google Scholar 

  11. Mathews, E.H., et al.: HVAC control strategies to enhance comfort and minimise energy usage. Energy Build. 33(8), 853–863 (2001). https://doi.org/10.1016/S0378-7788(01)00075-5

    Article  Google Scholar 

  12. Liu, H., Lee, S.C., Kim, M.J., Shi, H., Kim, J.T., Wasewar, K.L., Yoo, C.K.: Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system. Energy Build. 66, 553–561 (2013)

    Article  Google Scholar 

  13. Goyal, S., Ingley, H.A., Barooah, P.: Occupancy-based zone-climate control for energy-efficient buildings: complexity vs. performance. Appl. Energy 106, 209–221 (2013)

    Article  Google Scholar 

  14. Atacak, İ., Arıcı, N., Güner, D.: Modelling and evaluating air quality with fuzzy logic algorithm-Ankara-Cebeci sample. IJISAE 5(4), 263–268 (2017)

    Article  Google Scholar 

  15. Ari, S., Cosden, I.A., Khalifa, H.E., Dannenhoffer, J.F., Wilcoxen, P., Isik, C: Constrained fuzzy logic approximation for indoor comfort and energy optimization. In: NAFIPS 2005—annual meeting of the North American fuzzy information processing society (2005) https://doi.org/10.1109/nafips.2005.1548586

  16. Chen, W.-K., Wang, C.-T., Lin, M.-W: An experimental study on fuzzy control for indoor air quality and the energy consumption in a building. In: Proceeding of 2015 international conference on machine learning, Guangzhou. IEEE (2015). https://doi.org/10.1109/icmlc.2015.7340963

  17. Ghadi, Y.Y., Rasul, M.G., Khan, M.M.K.: Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland. Renew. Sustain. Energy Rev. 54, 738–744 (2016)

    Article  Google Scholar 

  18. Xiang, X., Yu, C., Lapierre, L., Zhang, J., Zhang, Q.: Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. Int. J. Fuzzy Syst. 20(2), 572–586 (2018). https://doi.org/10.1007/s40815-017-0401-3

    Article  MathSciNet  Google Scholar 

  19. Chang, C.-M., Chang, W.-J.: Robust fuzzy control with transient and steady-state performance constraints for ship fin stabilizing systems. Int. J. Fuzzy Syst. 21(2), 518–531 (2019)

    Article  MathSciNet  Google Scholar 

  20. Jang, J.-S.R.: ANFIS: adaptive network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 3 (1993)

    Article  Google Scholar 

  21. Murat, Y.S., Ceylan, H.: Use of artificial neural networks for transport energy demand modeling. Energy Policy 34, 3165–3172 (2006)

    Article  Google Scholar 

  22. Moon, J.W., Kim, J.J.: ANN-based thermal control models for residential buildings. Build. Environ. 45(7), 1612–1625 (2010). https://doi.org/10.1016/j.buildenv.2010.01.009

    Article  Google Scholar 

  23. Park, S., Kim, M., Kim, M., Namgung, H.-G., Kim, K.-T., Cho, K.H., Kwon, S.-B.: Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network. J. Hazard. Mater. 341, 75–82 (2018)

    Article  Google Scholar 

  24. Chaudhuri, T., Soh, Y.C., Li, H., Xie, L.: A feedforward neural network-based indoor-climate control framework for thermal comfort and energy saving in buildings. Appl. Energy 248, 44–53 (2019)

    Article  Google Scholar 

  25. Lei, L., Chen, W., Xue, Y., Liu, W.: A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network. Build. Environ. 162, 106296 (2019)

    Article  Google Scholar 

  26. Masumpoor, S., Yaghobi, H., Khanesar, M.A.: Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor. Expert Syst. Appl. 42, 6635–6647 (2015)

    Article  Google Scholar 

  27. Songa, S., Zhanga, B., Songb, X., Zhanga, Y., Zhang, Z., Li, W.: Fractional-order adaptive neuro-fuzzy sliding mode H∞ control for fuzzy singularly perturbed systems. J. Franklin Inst. 356, 5027–5048 (2019)

    Article  MathSciNet  Google Scholar 

  28. Atia, D.M., El-madany, H.T.: Analysis and design of greenhouse temperature control using ANFIS. J. Electr. Syst. Inf. Technol. 4, 34–48 (2017)

    Article  Google Scholar 

  29. Cervantes, J., Yu, W., Salazar, S., Chairez, I.: Takagi–Sugeno dynamic neuro-fuzzy controller of uncertain nonlinear system. IEEE Trans. Fuzzy Syst. 25(6), 1601–1615 (2017)

    Article  Google Scholar 

  30. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Adaptive neuro-fuzzy control of a spherical rolling robot using sliding-mode-control-theory-based online learning algorithm. IEEE Trans. Cybern. 43(1), 170–197 (2010)

    Article  Google Scholar 

  31. Fisk, W.J., De Almeida, A.T.: Sensor-based demand-controlled ventilation: a review. Energy Build. 29, 35–45 (1998)

    Article  Google Scholar 

  32. Nasr, N.A., Elsafty, A.F., Mosleh, M: CFD Investigation of carbon monoxide concentration within a ship vehicle garage. In: The American Society of Mechanical Engineering conference (ASME 2013). Verification & validation symposium, Las Vegas, Nevada (2013)

  33. Chan, Y.H.: Biostatistics 101: data presentation. Singapore Med. J. 44(6), 280–285 (2003)

    Google Scholar 

  34. Mohandes, S.R., Zhang, X., Mahdiyar, A.: A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing 340, 55–75 (2019)

    Article  Google Scholar 

  35. Agamy, H., Mosleh, M., Elserafy, K., Abdel Geliel, M., Abdel Rahman, N.: Neural Network Model of carbon monoxide distribution in onboard a RO–RO ship garage. PSERJ (2019). https://doi.org/10.21608/pserj.2019.13789.1003

    Article  Google Scholar 

  36. Abdel-Geliel, M., Khalil, A.: Adaptive fuzzy controller for loop control in a distributed control system. In: 2009 17th mediterranean conference on control and automation, 55–60 (2009) https://doi.org/10.1109/med.2009.5164514

  37. Singh, A.K., Chhabra, A., Chhillar, A., Ranga, A., Dahiya, R: Fuzzy logic based controllers for speed control of BLDC motor. Int. J. Res. Appl. Sci. Eng. Technol. 5(6) (2017)

  38. Annamraju, A., Nandiraju, S.: Robust frequency control in an autonomous microgrid: a two-stage adaptive fuzzy approach. Electr. Power Compon. Syst. 46(1), 83–94 (2018)

    Article  Google Scholar 

  39. Giannoutsos, S.V., Manias, S.N: Improving engine room ventilation systems. IEEE Ind. Appl. Mag. (2016). Digital Object Identifier https://doi.org/10.1109/mias.2015.2459088. https://www.IEEE.org/IAS

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Agamy, H., Abdelgeliel, M., Mosleh, M. et al. Neural Fuzzy Control of the Indoor Air Quality Onboard a RO–RO Ship Garage. Int. J. Fuzzy Syst. 22, 1020–1035 (2020). https://doi.org/10.1007/s40815-020-00804-1

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  • DOI: https://doi.org/10.1007/s40815-020-00804-1

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