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Team Members : Pramod Varshney, Can Isik, Chilukuri Mohan, H. Ezzat Khalifa, Onur Ozdemir, Ramesh Rajagopalan, Priyadip Ray, James Smith, Jensen Zhang. Management Of The Built Environment To Reduce Exposure Risk. Outline. SAC 2005 - Main Concerns Problem Definition

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Management of the built environment to reduce exposure risk l.jpg

Team Members: Pramod Varshney, Can Isik, Chilukuri Mohan, H. Ezzat Khalifa,

Onur Ozdemir, Ramesh Rajagopalan, Priyadip Ray,

James Smith, Jensen Zhang

Management Of The Built Environment To Reduce Exposure Risk


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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


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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…”


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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 (OSFALL) paradigm and move towards have-it-your-way (HYWAY) paradigm.


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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


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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


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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


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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,.


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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


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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


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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.


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CO2 Levels and Energy

  • For a single zone

    • CO2 concentration at steady state is known

    • The energy model is known*

      • Main energyconsumed 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


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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


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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)


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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


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Paradigm Shift from OSFA to HIYW

Increased system

complexity

OSFA

HIYW

Requires

  • Distributed Sensing

  • Distributed Communication

  • Distributed Actuation


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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


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Why Distributed Large-scale Sensor Networks?

  • Multiple sensors needed to acquire state information in each micro-environment, for implementation of HYWAY 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


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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 atleast 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


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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 estmation


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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


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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)


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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

Probability of detecting whether pollutant concentration exceeds a predefined threshold


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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


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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


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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.


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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)


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Our Approach (cont’d)

Illustrative Example

c

: Location of sensors

  • 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

The objective is the real time estimation

of concentration value at point C from the

neighboring sensors


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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


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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


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Outcomes

  • The “right number” of sensors can be determined to reduce cost

  • Development of multi-criteria, system-wide optimization methodologies for HYWAY 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


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Experimental CO2 Data - Location of Sensors

In collaboration with Reline Technology, India;

University of Technology, Sydney, Australia


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Experimental* CO2 Data – Concentration Levels

*More details about this experimental test-bed are provided in subsequent slides


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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


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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



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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



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Testbed Ready for Comparison 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)


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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


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SISO Control

  • Tref1 = 79 ºF

  • Tref2 = 77 ºF

  • 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


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Summary and Future Work

  • Optimization and control

    • Algorithms that enable us to move towards HYWAY 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


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Conclusion

  • “…a lot of the work was premature pending definition of a plausible problem scenario…”

    • We have presented a specific problem scenario in terms of the OSFA versus HIYW paradigm shift

  • “…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