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Investigation of real-life operating patterns of wood-burning appliances using stack temperature data.

Mahdi AhmadiJoshua R MinotGeorge AllenLisa Rector
Published in: Journal of the Air & Waste Management Association (1995) (2020)
A study was undertaken to identify patterns of consumer use of outdoor wood boilers or outdoor wood furnaces (technically referred to as outdoor wood-fired hydronic heaters (OWHHs)) and indoor wood stoves (IWSs) to inform the development of performance testing protocols that reflect real-life operating conditions. These devices are manually fed, and their usage protocols are a function of a number of variables, including user habits, household characteristics, and environmental factors. In this study, researchers logged the stack wall temperatures of 4 OWHH and 20 IWS units in the states of New York and Washington over two heating seasons. Stack wall temperature is an indicator of changes in combustion modes. Two algorithms were developed to identify usage modes and cold and warm start refueling events from the stack wall temperature time series. A linear correlation analysis was conducted to evaluate the effect of heat demand on usage patterns. The results and methods presented here will inform the cataloging of typical operational patterns of OWHHs and IWSs as a step in the development of performance testing procedures that represent actual in-home usage patterns.Implications: Current US regulatory programs for residential wood heating use a certification program to assess emissions and efficiency performance. Testing under this program uses a test that burns 100% of a single, standardized wood fuel charge to assess performance at different steady-state load conditions. This study assessed in-field operational patterns to determine if the current certification approach accurately characterized typical homeowner use patterns. The data from this study can be used to inform revisions to testing methods to increase certification test comparability between lab and field performance.
Keyphrases
  • air pollution
  • particulate matter
  • healthcare
  • cell wall
  • public health
  • quality improvement
  • big data
  • social media
  • deep learning
  • artificial intelligence