Abstract
When a chemical or biological agent is suddenly released into a ventilation system, its dispersion needs to be promptly and accurately detected. In this work, an optimization method for sensors layout in air ductwork was presented. Three optimal objectives were defined, i.e. the minimum detection time, minimum contaminant exposure, and minimum probability of undetected pollution events. Genetic algorithm (GA) method was used to obtain the non-dominated solutions of multiobjectives optimization problem and the global optimal solution was selected among all of the non-dominated solutions by ordering solutions method. Since the biochemical attack occurred in a ventilation system was a random process, two releasing scenarios were proposed, i.e. the uniform and the air volume-based probability distribution. It was found that such a probability distribution affected the results of optimal sensors layout and also resulted in different detect time and different probability of undetected events. It was discussed how the objective functions are being compatible and competitive with each other, and how sensor quantity affect the optimal results and computational load. The impact of changes on other parameters was given, i.e. the deposition coefficient, the air volume distribution and the manual releasing. This work presents an angle of air ductwork design for indoor environment protection and expects to help in realizing the optimized sensor system design for sudden contaminant releasing within ventilation systems.
Article PDF
Similar content being viewed by others
References
Ahmad MW, Mourshed M, Yuce B, Rezgui Y (2016). Computational intelligence techniques for HVAC systems: A review. Building Simulation, 9: 359–398.
Arvelo J, Brandt A, Roger RP, Saksena A (2002). An enhanced multizone model and its application to optimum placement of CBW sensors. ASHRAE Transactions, 108(2): 818–825.
Bastani A, Haghighat F, Kozinski JA (2012). Contaminant source identification within a building: Toward design of immune buildings. Building and Environment, 51: 320–329.
Bazargan-Lari MR (2014). An evidential reasoning approach to optimal monitoring of drinking water distribution systems for detecting deliberate contamination events. Journal of Cleaner Production, 78: 1–14.
Carr RD, Greenberg H J, Hart W E, Konjevod G, Lauer E, Lin H, Morrison T, Phillips CA (2006). Robust optimization of contaminant sensor placement for community water systems. Mathematical Programming, 107: 337–356.
Chang N-B, Prapinpongsanone N, Ernest A (2012). Optimal sensor deployment in a large-scale complex drinking water network: Comparisons between a rule-based decision support system and optimization models. Computers & Chemical Engineering, 43: 191–199.
Chen C, Lin C-H, Long Z, Chen Q (2014). Predicting transient particle transport in enclosed environments with the combined CFD and Markov chain method. Indoor Air, 24: 81–92.
Chen YL, Wen J (2008). Sensor system design for building indoor air protection. Building and Environment, 43: 1278–1285.
Chen YL, Wen J (2010). Comparison of sensor systems designed using multizone, zonal, and CFD data for protection of indoor environments. Building and Environment, 45: 1061–1071.
Chen YL, Wen J (2012). The selection of the most appropriate airflow model for designing indoor air sensor systems. Building and Environment, 50: 34–43.
Das P, Shrubsole C, Jones B, Hamilton I, Chalabi Z, Davies M, Mavrogianni A, Taylor J (2014). Using probabilistic sampling-based sensitivity analyses for indoor air quality modelling. Building and Environment, 78: 171–182.
Deb K, Kalyanmoy D (2001). Multi-Objective Optimization Using Evolutionary Algorithms. London: John Wiley & Sons.
Eliades DG, Michaelides MP, Panayiotou CG, Polycarpou MM (2013). Security-oriented sensor placement in intelligent buildings. Building and Environment, 63: 114–121.
Feustel HE (1999). COMIS—An international multizone air-flow and contaminant transport model. Energy and Buildings, 30: 3–18.
Fontanini AD, Vaidya U, Ganapathysubramanian B (2016). A methodology for optimal placement of sensors in enclosed environments: A dynamical systems approach. Building and Environment, 100: 145–161.
Gao J, Zeng L, Wu L, Ding X, Zhang X (2016). Solution for sudden contamination transport through air duct system: Under a puff release. Building and Environment, 100: 19–27.
Glover NJ (2002). Countering chemical and biological terrorism. Civil Engineering, 72(5): 62–67.
Kowalski W, Bahnfleth W, Musser A (2003). Modeling immune building systems for bioterrorism defense. Journal of Architectural Engineering, 9: 86–96.
Krause A, Leskovec J, Guestrin C, Van Briesen J, Faloutsos C (2008). Efficient sensor placement optimization for securing large water distribution networks. Journal of Water Resources Planning and Management, 134: 516–526.
Liu X, Zhai Z (2009). Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods. Building and Environment, 44: 2276–2283.
Meier RW, Barkdol BD (2000). Sampling design for network model calibration using Genetic Algorithms. Journal of Water Resources Planning and Management, 126: 245–250.
Ostfeld A, Salomons E (2004). Optimal layout of early warning detection stations for water distribution systems security. Journal of Water Resources Planning and Management, 130: 377–385.
Ostfeld A, Uber JG, Salomons E, Berry JW (2008). The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms. Journal of Water Resources Planning and Management, 134: 556–568.
Pearson GS (2011). Bioterrorism Preparedness: The United Kingdom Approach. In: Katz R, Zilinskas BA (eds), Encyclopedia of Bioterrorism Defense, 2nd edn. London: John Wiley & Sons.
Preis A, Ostfeld A (2008). Multiobjective contaminant sensor network design for water distribution systems. Journal of Water Resources Planning and Management, 134: 366–377.
Shastri Y, Diwekar U (2006). Sensor placement in water networks: a stochastic programming approach. Journal of Water Resources Planning and Management, 132: 192–203.
Sohn MD, Lorenzetti DM (2007). Siting bio-samplers in buildings. Risk Analysis, 4: 877–886.
Sohn MD, Reynolds P, Singh N, Gadgil AJ (2011). Rapidly locating and characterizing pollutant releases in buildings. Journal of the Air & Waste Management Association, 52: 1422–1432.
Sreedharan P, Sohn MD, Gadgil AJ, Nazaroff WW (2006). Systems approach to evaluating sensor characteristics for real-time monitoring of high-risk indoor contaminant releases. Atmospheric Environment, 40: 3490–3502.
Sreedharan P, Sohn MD, Nazaroff WW, Gadgil AJ (2007). Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases. Atmospheric Environment, 41: 9530–9542.
Sreedharan P, Sohn MD, Nazaroff WW, Gadgil AJ (2011). Towards improved characterization of high-risk releases using heterogeneous indoor sensor systems. Building and Environment, 46: 438–447.
Thompson BP, Bank LC (2008). Survey of bioterrorism risk in buildings. Journal of Architectural Engineering, 14: 7–17.
Thompson BP, Bank LC (2010). Use of system dynamics as a decisionmaking tool in building design and operation. Building and Environment, 45: 1006–1015.
Walter T, Lorenzetti DM, Sohn MD (2012). Siting samplers to minimize expected time to detection. Risk Analysis, 32: 2032–2042.
Watson JP, Murray R, Hart WE (2009). Formulation and optimization of robust sensor placement problems for drinking water contamination warning system. Journal of Infrastructure Systems, 15: 330–339.
Xue Y, Zhai ZJ (2017). Inverse identification of multiple outdoor pollutant sources with a mobile sensor. Building Simulation, 10: 255–263.
You S, Wan MP (2014). Particle concentration dynamics in the ventilation duct after an artificial release: For countering potential bioterrorist attack. Journal of Hazardous Materials, 267: 183–193.
Zhang T, Chen QY, Lin C-H (2007). Optimal sensor placement for airborne contaminant detection in an aircraft cabin. HVAC&R Research, 13: 683–696.
Zhang T, Yin S, Wang S (2013). An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source. Atmospheric Environment, 77: 62–77.
Zhao B, Li X, Yan Q (2003). A simplified system for indoor airflow simulation. Building and Environment, 38: 543–552.
Zhao B, Wu J (2006). Modeling particle deposition onto rough walls in ventilation duct. Atmospheric Environment, 40: 6918–6927.
Zhou B, Zhao B, Tan Z (2011). How particle resuspension from inner surfaces of ventilation ducts affects indoor air quality—A modeling analysis. Aerosol Science and Technology, 45: 996–1009.
Zhou P, Huang G, Zhang L, Tsang KF (2015). Wireless sensor network based monitoring system for a large-scale indoor space: Data process and supply air allocation optimization. Energy and Buildings, 103: 365–374.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 51278370).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gao, J., Zeng, L., Cao, C. et al. Multi-objective optimization for sensor placement against suddenly released contaminant in air duct system. Build. Simul. 11, 139–153 (2018). https://doi.org/10.1007/s12273-017-0374-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-017-0374-z