Distributed Contaminant Detection and Isolation for Intelligent Buildings

The automatic preservation of the indoor air quality (IAQ) is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The IAQ, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this paper, we provide the detailed analysis, implementation, and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems, and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this paper is the investigation of the impact of the partitioning solution on the performance of the distributed contaminant detection and isolation (CDI) scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed CDI methodology is demonstrated using the models of real building case studies created on CONTAM.11CONTAM is a multizone simulation program developed by the U.S. National Institute of Standards and Technology.

[1]  Derong Liu,et al.  Adaptive approximation based control: Unifying neural, fuzzy and traditional adaptive approximation approaches. Jay A. Farrell and Marios M. Polycarpou, Wiley, New York, 2006. No of pages: 440. ISBN 978-0-471-72788-0 , 2008 .

[2]  Shugang Wang,et al.  Inversely tracking indoor airborne particles to locate their release sources , 2012 .

[3]  Marios M. Polycarpou,et al.  Partitioning of Intelligent Buildings for Distributed Contaminant Detection and Isolation , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[4]  D. Clements–Croome,et al.  Past , present and future mathematical models for buildings FOCUS ON INTELLIGENT BUILDINGS ( PART 1 ) , 2018 .

[5]  Thomas Parisini,et al.  Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems , 2011, Eur. J. Control.

[6]  J.E. Braun,et al.  Intelligent Building Systems - Past, Present, and Future , 2007, 2007 American Control Conference.

[7]  X Liu,et al.  Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals. , 2007, Indoor air.

[8]  Derek Clements-Croome,et al.  Past, present and future mathematical models for buildings , 2009 .

[9]  Michael D. Sohn,et al.  Evaluating sensor characteristics for real-time monitoring of high-risk indoor contaminant releases , 2006 .

[10]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[11]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[12]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[13]  Fenghao Wang,et al.  Characterizing transportation of indoor gaseous contaminant using the state space method , 2010 .

[14]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[15]  George Walton,et al.  Contam 2. 4 User Guide and Program Documentation , 2018 .

[16]  Andrew K. Persily,et al.  Airflow and Indoor Air Quality Models of DOE Reference Commercial Buildings , 2012 .

[17]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[18]  Marios M. Polycarpou,et al.  Multi-constraint building partitioning formulation for effective contaminant detection and isolation , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[19]  Min Li,et al.  State-space model for airborne particles in multizone indoor environments , 2008 .

[20]  Marios M. Polycarpou,et al.  A Distributed Architecture for HVAC Sensor Fault Detection and Isolation , 2015, IEEE Transactions on Control Systems Technology.

[21]  Kasper Grann Andersson.  On the current needs in European decision support tools for contaminated areas , 2012 .

[22]  S. T. Parker,et al.  State-space methods for calculating concentration dynamics in multizone buildings , 2011 .

[23]  Liangzhu Wang,et al.  Evaluation of some assumptions used in multizone airflow network models , 2008 .

[24]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[25]  Yunjun Xu,et al.  Evacuation Modeling From the Control Perspective and Corresponding Sequential-Based Optimal Evacuation Guidance , 2014, IEEE Transactions on Control Systems Technology.

[26]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[27]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[28]  Christos Panayiotou,et al.  A distributed framework for contaminant event detection and isolation in multi-zone intelligent buildings , 2014, 2014 European Control Conference (ECC).

[29]  Jelena Srebric,et al.  Real-Time Identification of Indoor Pollutant Source Positions Based on Neural Network Locator of Contaminant Sources and Optimized Sensor Networks , 2010, Journal of the Air & Waste Management Association.

[30]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[31]  M. Sohn,et al.  Rapidly Locating and Characterizing Pollutant Releases in Buildings , 2000, Journal of the Air & Waste Management Association.

[32]  Jelena Srebric,et al.  Application of Neural Networks Trained with Multi-Zone Models for Fast Detection of Contaminant Source Position in Buildings , 2007 .

[33]  Christos Panayiotou,et al.  Automatic building partitioning for effective contaminant detection and isolation , 2016, 2016 18th Mediterranean Electrotechnical Conference (MELECON).

[34]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[35]  A J Gadgil,et al.  Responding to sudden pollutant releases in office buildings: 1. Framework and analysis tools. , 2003, Indoor air.

[36]  J. Farrell,et al.  Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) , 2006 .

[37]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[38]  Zhiqiang John Zhai,et al.  Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling , 2009 .

[39]  Olivier Sename,et al.  A Hybrid Model and MIMO Control for Intelligent Buildings Temperature Regulation over WSN , 2009 .

[40]  Liangzhu Wang,et al.  Forecasting simulations of indoor environment using data assimilation via an Ensemble Kalman Filter , 2013 .

[41]  Neil Genzlinger.  A. and Q , 2006 .

[42]  Qi Zhang,et al.  Fault Detection and Isolation of the Wind Turbine Benchmark: an Estimation-based Approach , 2011 .

[43]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[44]  Marios M. Polycarpou,et al.  A distributed fault detection filtering approach for a class of interconnected continuous-time nonlinear systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[45]  X Liu,et al.  Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling. , 2007, Indoor air.

[46]  Manuel Roveri,et al.  A Cognitive Monitoring System for Detecting and Isolating Contaminants and Faults in Intelligent Buildings , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[47]  Kevin Weekly,et al.  Modeling and estimation of the humans' effect on the CO2 dynamics inside a conference room , 2014, CDC.

[48]  Fariborz Haghighat,et al.  Contaminant source identification within a building: Toward design of immune buildings , 2011, Building and Environment.

[49]  Marios M. Polycarpou,et al.  Security-oriented sensor placement in intelligent buildings , 2013 .

[50]  W. Marsden.  I and J , 2012 .

[51]  Marios M. Polycarpou,et al.  A Distributed Fault Detection Filtering Approach for a Class of Interconnected Continuous-Time Nonlinear Systems , 2013, IEEE Transactions on Automatic Control.

[52]  Qingyan Chen,et al.  Using CFD Capabilities of CONTAM 3.0 for Simulating Airflow and Contaminant Transport in and around Buildings , 2010 .

[53]  Maria Letizia Corradini,et al.  IEEE Transactions on Control Systems Technology , 2004 .

[54]  Marios M. Polycarpou,et al.  Contaminant event monitoring in multi-zone buildings using the state-space method , 2014 .