Conference Paper Session 5A Characterization of IAQ Performance of Products and Systems 3

Tuesday, September 13, 2016: 9:00 AM-10:30 AM
Chair: Chandra Sekhar, Ph.D., National University of Singapore
To come

1  Toward Making Ventilation Decisions Based on Expected Outcomes: A Flexible Multi-Criteria Framework

Adams Rackes, Drexel University
Tom Ben-David, Drexel University
Michael S. Waring, Ph.D., Drexel University
Next-generation ventilation should explicitly aim to aggregate, compare, weight, and ultimately integrate ventilation’s numerous impacts, which include the dilution of multiple indoor pollutants, introduction of multiple outdoor pollutants, direct correlations to productivity and absenteeism, and strong influences on energy consumption, peak power demand, and their associated environmental impacts. We propose a multi-criteria decision-making framework based on (i) a comprehensive, scientific accounting of the costs and benefits of ventilation, and (ii) a utility maximization formulation to allow both expression of user preferences and adjustment for uncertainty. The utility function (UF) provides the core criterion and interface to enable more intelligent and holistic ventilation, allowing leveraging of new and future capabilities, including optimized and predictive methods that make use of models or learning, dynamic control that capitalizes on transient effects, and real-time information from new sensors or interconnected information networks and energy grids. We first formulate the UF to be as expansive as possible in scope of impacts, and then conduct a series of sensitivity analyses (SA) to help refine it and identify building parameters that impact it. For the SA data, we made use of an existing simulation-based analysis of eight off-the-shelf ventilation and related strategies that combined economizing, demand-controlled ventilation, and supply air temperature reset. Outcomes were reprocessed to a daily-average basis, and included: electricity consumption, natural gas consumption, median ventilation rates, relative symptom prevalence, relative work performance, and concentrations of carbon dioxide, total volatile organic compounds, and fine particulate matter and ozone of outdoor origin. We first ask, which outcomes are ultimately worth including in the UF? We employ: a principal component analysis of outcomes to check if any are redundant; SA of the impact of each individual outcome on total utility over a range of reasonable user preferences; and an analysis of which outcomes can themselves be meaningfully affected by ventilation. We next turn to an initial evaluation of which building parameters will need to be taken into account by a dynamic strategy that is attempting to maximize utility. We conduct a separate SA where the dependent variable is the daily average ventilation rate observed under each off-the-shelf dynamic strategy, and the independent variables are ~20 varied building parameters as well as 10 explanatory covariates. This task is a crucial first step in simplifying the challenge of generalizing optimal ventilation beyond the case-study level, in the face of tremendous variability among real building and setting characteristics.

2  Measured Space-Conditioning Energy and Indoor RH in a Mechanically-Ventilated Lab Home with Fixed and Variable-Capacity Cooling Systems Located in a Hot and Humid Climate

Charles Withers Jr., Florida Solar Energy Center
Residential whole-house mechanical ventilation has become more important as the impetus has been made to construct homes with less air leakage. Requirements for homes to meet minimum air tightness requirements and to be equipped with mechanical ventilation have even been made mandatory under certain building programs and codes. Homes mechanically ventilated during warm and humid weather will have elevated indoor relative humidity (RH) during low cooling load periods. This requires supplemental dehumidification for at least some low load periods to maintain acceptable RH control. Herein lies a challenge to balance acceptable RH with minimal energy use. A research project was completed to evaluate three specific types of space cooling equipment test configurations in a controlled research lab home. The lab home was furnished and had three bedrooms and two bathrooms located in central Florida. It had automated internal sensible and latent loads, and was ventilated in accordance with ASHRAE 62.2-2013. The main goal of the project was to contribute to the limited body of research seeking to balance space conditioning energy efficiency with good RH control in continuously mechanically ventilated homes, particularly in the hot and humid climate zones of the United States. The focus of the testing was to evaluate space cooling and dehumidifier energy use as well as the resulting indoor RH throughout the home. The three primary test configurations covered in this paper involved: 1) a central ducted fixed-capacity SEER 13 rated system 2) a central ducted variable-capacity SEER 22 rated system, and 3) a SEER 21.5 ductless variable-capacity minisplit. The minisplit was operated as the primary cooling system with central system used for cooling backup during near peak cooling load periods. The project found that the SEER 22 central system configuration used 20% less energy than the SEER 13 central system, and the mini-split configuration used 25% less energy than the SEER 13 system under typical seasonal conditions. Limited supplemental dehumidification was needed to maintain indoor RH below 60% during some low cooling load periods. When needed, dehumidifier use was typically only 1-3 cycles per day (0.18 – 0.58 kWh/day). This paper shares greater details on the variability of indoor RH among the test configurations, factors that resulted in the very limited need for supplemental dehumidification, and recommendations to improve latent performance of variable capacity cooling systems.

3  Modeling Monetization of Collateral IAQ Improvements from UVGI for Coil Cleaning

Joseph Firrantello, P.E., Pennsylvania State University
William Bahnfleth, Ph.D., P.E., Pennsylvania State University
Ultraviolet Germicidal Irradiation (UVGI) of cooling coils is done to control biofouling that can increase their flow resistance and decrease heat transfer coefficient. UVGI is also applied in air-handling units to improve indoor air quality (IAQ) by deactivating airborne microorganisms. A typical coil cleaning application delivers a smaller UV dose than an air treatment system, but should provide some collateral air treatment benefit. To date, this effect has not been studied. In this investigation, the benefit of air treatment provided by a cooling coil irradiation system is estimated via simulations employing a subset of the DOE Commercial Reference Buildings library. Benefits are quantified in terms of appropriate measures for each building type: reduced work-loss days (WLD) for office buildings, reduced disability adjusted life years (DALY) for schools, and reduced hospital acquired infections (HAI) for healthcare facilities. UVGI sized for coil cleaning results in a 2% to 12% average reduction in the measure of interest for each building. This reduction is negatively correlated with the average outdoor air fraction in each building type. Combining WLDs with US Gross National Income to monetize savings for Small, Medium, and Large Office Buildings yields between $2.10/m2 and $6.61/m2, $0.36/m2 and $3.04/m2, and $0.04/m2 and $0.55/m2 respectively. Combining DALYs with US Gross National Income to monetize savings for Primary and Secondary Schools results a wide range: $0.01/m2 to $1.93/m2 due to the large range of values one might reasonably assign to a DALY. In hospitals, Reduction in airborne HAIs resulted in estimated savings of $0.13/m2 to $0.62/m2.

4  Data Driven Persistent Monitoring of Indoor Air Systems

Sambuddha Ghosal, Iowa State University
Chao Liu, Ph.D., Iowa State University
Ulrike Passe, AIA, Iowa State University
Shan He, Iowa State University
Soumik Sarkar, Ph.D., Iowa State University
Data-driven persistent monitoring of indoor air systems:

Persistent monitoring of Indoor Air Quality (IAQ) within and around buildings and structures is critical to reduce risk of indoor health concerns. Specifically, IAQ issues in large integrated buildings may stem from inadequate ventilation and/or faults in the complex HVAC systems that together with control and communication systems can be considered as complex Cyber Physical Systems (CPSs). We propose a data-driven framework for monitoring distributed complex CPSs that reliably captures cyber and physical sub-system behaviors as well as their interaction characteristics. Using such learning methods, we aim to identify the anomalies and faults at an early stage such that necessary mitigation measures can be pursued in time. A fault in the HVAC system may be due to both physical and cyber anomalies affecting the operational goals of the building system. The proposed technique involves modeling of cyber and physical entities using probabilistic graphical models that capture individual characteristics of the sub-system and causal dependencies among different sub-systems. The proposed model can be trained using nominal historical data and then can be used to monitor the HVAC system and IAQ during regular operation. Our method is validated with a case study on an integrated “zero energy” house built for the 2009 Solar Decathlon that has been used both as an experimental test bed and office for more than 3 years.

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