Management Of The Built Environment To Reduce Exposure Risk Team Members: Pramod Varshney, Can Isik, Chilukuri Mohan, H. Ezzat Khalifa, Onur Ozdemir, Ramesh Rajagopalan, Priyadip Ray, James Smith, Jensen Zhang

Outline SAC 2005 - Main Concerns Problem Definition Motivation and Objectives Research Needs Integrated Components of this Task Optimization and Control Indoor Sensor Networks Testbeds Summary and Future Work

SAC 2005 - Main Concerns “…a lot of the work was premature pending definition of a plausible problem scenario…” “…an approach based on temperature or CO2 control might be feasible but should only be considered if the state of the art in indoor environmental controls will be advanced…” “…necessary to refocus this effort…” “…consult with practicing HVAC engineers, and make inquiries from professionals in the industry…”

The Problem Definition How can we improve the indoor air quality (IAQ) around each individual in a built environmental system (BES) while keeping the cost at a reasonable level? Treat built environment as a collection of multiple controllable zones Shift from one-size-fits all (OSFA) paradigm and move towards have-it-your-way (HIYW) paradigm.

There is no Free Lunch! Improved health, productivity and comfort, at the expense of increased system complexity: Additional infrastructure Increase in cost, computation, communication and actuation Additional burden of coupling effect between zones

Motivation and Objectives Existing “one size fits all” solutions leave many occupants dissatisfied with their environments, limiting productivity and affecting health “New research shows that higher IAQ improves health, learning and productivity” - Dr. Ole Fanger Empowering each occupant with the ability to control one’s own environment improves satisfaction and productivity; expected technological changes will help realize this vision. This will require a paradigm shift in HVAC technology “I predict a dramatic change in HVAC technology in the future” - Dr. Ole Fanger

Research Needs Controlling individual environments while keeping the cost at a reasonable level is an optimization problem, whose solution requires: Measuring environmental parameters at an individual level with a complex network of sensors Providing control actuators at an individual scale but with coordination Reacting to changes in the system variables such as occupancy and weather conditions In order to customize the IAQ, a network of sensors, controllers and actuators is needed. A wireless network allows low-cost retrofitting of existing buildings.

Goal Implement cost effective personal control of the microenvironment, which enhances individual health, satisfaction and productivity, by integrating sensing, intelligent information processing and distributed control.

Integrated Components of this Task Improve indoor air quality by: Optimization and control – new methodologies for real-time control Indoor sensor network – design, placement, data processing, and spatio-temporal profiling Test-beds – design of new test-beds and implementation of developed methodologies on these test-beds

Micro-Level Demand-Controlled Ventilation (DCV) Main Objective: To improve IAQ around each individual in an office building Approach: Develop optimization algorithms that will improve IAQ in every single office for each individual* Constraints: Energy consumption, costs and individual comfort * Ari, I.A. Cosden, H.E. Khalifa, J. F. Dannenhoffer, P. Wilcoxen, C. Isik, ”Constrained Fuzzy Logic Approximation for Indoor Comfort and Energy Optimization”, 24th International Conference of the North American Fuzzy Information Processing Society (NAFIPS 2005) Proceedings, Ann Arbor, Michigan, June 2005

DCV At The Micro Level Emissions in a room*: CO2 - surrogate for emissions by people or through human activity TVOC – surrogate for emissions from room contents and furniture Our Focus: CO2 levels as the IAQ criterion for optimization Goal: Optimization of ventilation rates (CO2 levels) and energy costs in a multi-zone BES Approach: DCV at the micro-level with one controllable diffuser in each room (e.g., Variable Air Volume – VAV) * ASHRAE Std. 62 2004 allows for ventilation rates based on both occupancy and floor area. A DCV system based only on CO2 will address the people component but not the passive component.

CO2 Levels and Energy For a single zone CO2 concentration at steady state is known The energy model is known* Main energy consumed by the HVAC is proportionally related to the cooling/heating coil load * S.Atthajariyakul, T. Leephakpreeda,”Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage”, Energy and Buildings, vol.36, pp. 720-733, 2004

Illustrative Example An office building with 25 rooms: Intra-room uniformity: One temperature and RH value assumed within each room(well-mixed conditions) Inter-room uniformity: All rooms have identical DBT and RH values Occupancy: 82 people in the building, with 1-5 people in each room Goal: Find optimum outside airflow rates (Fo) for each room, with: Minimal energy consumption CO2 levels Ci < 800 ppm threshold in each room

Preliminary Results For the same total airflow rate, we compare two alternatives: OSFA solution: Same airflow rate (Fo) in each office HIYW solution: Fo adjusted using occupancy information (room-level DCV)

# of Occupants for whom Ci > 800 ppm Preliminary Results Improved IAQ at the same energy cost Future Work: Optimization based on airflow dynamics between rooms (Task3.2), variable indoor air temperatures, occupancy variations and variable metabolic rates DCV at the Micro Level (HIYW) OSFA Solution Power Consumption (kW) 56.6 # of Occupants for whom Ci > 800 ppm 56 of 82 # of Rooms where Ci>800 ppm 13 of 25

Paradigm Shift from OSFA to HIYW Requires Distributed Sensing Distributed Communication Distributed Actuation OSFA HIYW Increased system complexity

Integrated Components of this Task Improve indoor air quality by Optimization and control – improved IAQ via new methodologies for real-time control Indoor sensor network – design, placement, data-processing and spatio-temporal profiling Test-beds – design of new test-beds and implementation of developed methodologies on these test-beds

Why Distributed Large-scale Sensor Networks? Multiple sensors needed to acquire state information in each micro-environment, for implementation of HIYW paradigm Higher resolution and fidelity data available in a sensor-rich environment will improve distributed monitoring Sensors need to be networked for system-wide optimization and real-time control of i-BES Wireless networks facilitate lower-cost retrofitting of existing buildings

Previous Work N. Lin, C. Federspiel and D. Auslander, “Multi-sensor Single-Actuator Control of HVAC Systems”, Int. Conf. For Enhanced Building Operations, Richardson, TX, 2002 Simulation results demonstrating the advantage of using at least one sensor for each room compared to one sensor for many rooms Assumes a single actuator Wang, D. E., Arens, T. Webster, and M. Shi. "How the Number and Placement of Sensors Controlling Room Air Distribution Systems Affect Energy Use and Comfort." International Conference for Enhanced Building Operations, Richardson, TX, October, 2002 Simulations results showing the benefits of using more than one temperature sensor to control conditions in the occupied zone of a room

Previous Work (cont’d) H.Zhang, B.Krogh, J.F. Moura and W.Zhang,”Estimation in virtual sensor-actuator arrays using reduced-order physical models”, 43rd IEEE Conference on Decision and Control, December 14-17,Atlantis, 2004 Application of sensor networks for real-time estimates of the values of a distributed field at points where there are no sensors Assumes linear system models Clifford C. Federspiel, “Estimating the Inputs of Gas Transport Processes in Buildings”, IEEE Trans. on Control Systems Technology, 1997 Estimation of the strength of a gas source in an enclosure Applies Kalman filtering for state estimation

Multi-sensor Detection and Sensor Placement Application of multiple sensors for improved detection of indoor pollutants Improved detection enables reduced exposure of occupants to pollutants Development of cost-effective sensor placement strategies for improved indoor air quality* * S.L.Padula and K.R.Kincaid, “Optimization Strategies for Sensor and Actuator Placement”,NASA LaRC Technical Library Digital Repository, http://hdl.handle.net/2002/14468.

Multi-sensor Detection – Example Simulated concentration profile of a gaseous pollutant released in still air at a point in a room of 9m x 10m x 6m dimensions with source located at (5,5,0)

Numerical Example An example demonstrating the utility of multiple sensors for fast detection of a pollutant. The simulations are for a room with source located at the center of the room Detection Probability: Probability of detecting whether pollutant concentration exceeds a predefined threshold

Sensor Placement Problem Goal: To determine optimal locations of sensors for detection of gaseous pollutants in a room or a large hall Evaluation measure: Improved probability of detection of gaseous indoor air pollutants Constraint: Number of sensors to be deployed We assume a spatial probability distribution for the location of the source. Approaches: Uniform placement where sensors are equi-spaced from each other Closest point placement where sensors are placed at locations closest to the mean of the spatial source distribution Intelligent placement strategies for quick pollutant detection and reduction of exposure time

Sensor placement results with 3 sensors placed in a 9x10x6m room Simulation Results Sensor placement results with 3 sensors placed in a 9x10x6m room Intelligent placement strategy outperforms intuitive strategies such as uniform placement in terms of the detection probability Approach Detection probability Uniform placement 0.35 Closest point placement 0.68 Intelligent placement strategy (Evolutionary algorithm with local search) 0.97

Spatio-temporal Profiling of Environ. Parameters In environmental applications, sensor networks monitor physical variables governed by continuous distributed dynamics Results in correlated sensor observations Difficulty: Spatial and temporal irregularities in sampling Problem: Produce real-time estimates of the values of a distributed field at points where there are no sensors* * H.Zhang, B.Krogh, J.F. Moura and W.Zhang,”Estimation in virtual sensor-actuator arrays using reduced-order physical models”, 43rd IEEE Conference on Decision and Control, December 14-17,Atlantis, 2004.

Our Approach We propose a supervised local function learning approach to estimate the observation of a sensor from a subset of its neighbors The goal is to estimate an unknown continuous-valued function in the relationship y = g(X) + n where, the random error/noise (n) is zero-mean, X is a d-dimensional vector (e.g., Position coordinates of sensors) and y is a scalar output (e.g., concentration of CO2)

Our Approach (cont’d) A generalized regression neural network (GRNN) has been used, due to its superior interpolation abilities and fast convergence GRNN learns the spatial concentration function from the observations provided by sensors in the neighborhood (circular region) of a desired location c : Location of sensors Illustrative Example The objective is the real time estimation of concentration value at point C from the neighboring sensors

Simulation Results for Spatial Profiling of CO2 - 1/2 Estimation of the CO2 levels at each microenvironment from sparsely distributed sensors One realization of the spatial profile of CO2 and location of sensors

Simulation Results for Spatial Profiling of CO2 – 2/2 How many sensors are adequate ? Summary : About 70 sensors are adequate for MSE of 0.06; additional sensors do not significantly improve MSE

Outcomes The “right number” of sensors can be determined to reduce cost Development of multi-criteria, system-wide optimization methodologies for HIYW systems Multiple, interdependent, individually customized microenvironments controlled by distributed environmental control systems (e.g., PVDs) will be aided by the fine-grained characterization of IAQ parameters Future Work: Reduced order models for building inter-zonal transport (Task 3.2) will provide more realistic and computationally faster models to test our algorithms

Experimental CO2 Data - Location of Sensors In collaboration with Reline Technology, India; University of Technology, Sydney, Australia

Experimental* CO2 Data – Concentration Levels *More details about this experimental test-bed are provided in subsequent slides

Integrated Components of this Task Improve indoor air quality by Optimization and control – improved IAQ via new methodologies for real-time control Indoor sensor network – design, placement,data-processing and spatio-temporal profiling Test-beds – design of new test-beds and implementation of developed methodologies on these test-beds

Sensor Network Testbed in Link Hall Goal : Data collection and evaluation platform for various control and data-processing algorithms being developed Wireless sensor network test-bed is being set-up on 3rd floor of Link Hall Four closed spaces/rooms on the third floor of Link Hall will be monitored by the WSN Each closed space will have 5 ABLE ARH-T-2-I-W temperature & RH sensors, 5 TI 4GS CO2 sensors,1 pressure sensor and a information processing unit called “Sensor Network Access Point (SNAP)” In future this test-bed will be incorporated in the building control system for evaluation of the complete system

Sensor Network Test-bed in Link Hall (cont’d) Possible Locations of SNAPs

ICUBE Lab (2007) Goal: Allow people to adjust their own indoor air parameter settings within an office or a cubicle while maximizing overall energy usage Giving people (users of the lab) what they want for control settings without increasing baseline costs for the larger space The lab will consist of one test lab (with uniform settings) and one control lab (with individual settings) The lab will feature intelligent controls, sensor networks The lab will employ raised floor diffusers with actuated damper controls The lab will behave like a real building, with people able to adjust their own thermostat settings Multiple configurations will be possible: classroom to office to lab

ICUBE Lab

Testbed Ready for Evaluation of New Algorithms Main Goal: Develop and test multiple-input multiple-output (MIMO) control algorithms for intelligent HVAC control DAQ system and the actuators have been installed on the HVAC demonstrator (Hampden H-ACD-2-CDL) in Link-0031 System Characterization Experiments (full capability) Single-Input Single-Output (SISO) Control Experiments (full capability) MIMO Control Experiments with combination of CO2, TVOC and temperature control (technically ready, awaiting CO2 and TVOC sensors)

System Characterization SISO Temperature Open Loop Response Illustration of DAQ on the HVAC demonstrator Enables system characterization so that control algorithms can be developed* * M.L. Anderson, M.R. Buehner, P.M. Young, D.C. Hittle, C. Anderson, J. Tu, and D. Hodgson, “An Experimental System for Advanced Heating, Ventilating, and Air Conditioning (HVAC) Control”, to appear in Energy and Buildings, 2006

SISO Control SISO Temperature Control Illustration of developed SISO controller on the HVAC demonstrator Future Work: Develop and test MIMO controllers that will enable us to overcome coupling effects existing in today’s state-of-the-art HVAC systems resulting in improved performance* * M.L. Anderson, M.R. Buehner, P.M. Young, D.C. Hittle, C. Anderson, J. Tu, and D. Hodgson, “MIMO Robust Control for Heating, Ventilating, and Air Conditioning (HVAC) Systems”, submitted to IEEE Transactions on Control Systems Technology, 2005 Tref1 = 79 ºF Tref2 = 77 ºF

Summary and Future Work Optimization and control Algorithms that enable us to move towards HIYW paradigm without compromising comfort and energy costs are being developed More complex and realistic algorithms will be developed and tested on real test-beds Indoor sensor network Algorithms are being developed for improved multi-sensor detection, sensor placement and spatio-temporal profiling Algorithms will be tested on more realistic models (obtained from Task 3.2) and actual test-beds (Task 5) Test-beds Work is in progress on the set-up of sensor network test-beds Work is in progress on the set-up of HVAC test-beds

Conclusion “…a lot of the work was premature pending definition of a plausible problem scenario…” We have presented a specific problem scenario involving the shift from OSFA to HIYW paradigm “…an approach based on temperature or CO2 control might be feasible but should only be considered if the state of the art in indoor environmental controls will be advanced…” We are focusing on temperature and CO2 control at the micro-environment level (personal level) “…necessary to refocus this effort…” Significant refocusing of the effort as detailed in the presentation has been done “…consult with practicing HVAC engineers, and make inquiries from professionals in the industry…” We have initiated collaboration with United Technologies Research Center