Department of Architectural Engineering, Pennsylvania State University, University Park, PA 16802, USA.
J Air Waste Manag Assoc. 2010 Sep;60(9):1034-48. doi: 10.3155/1047-3289.60.9.1034.
A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.
人们越来越关注安全和居住者接触污染物的问题，这就需要在偶然情况下快速、可靠地识别污染物的来源。为了确定室外环境中潜在的污染物源位置，目前最先进的建模方法是在并行处理器上进行计算流体动力学模拟。在室内环境中，当前的工具将偶然的污染物分布与可能浓度分布的预计算数据库中的情况进行匹配。这些方法在预处理和后处理中都需要大量的计算。另一方面，神经网络已经成为快速预测室外环境污染物（如氮氧化物或二氧化硫）浓度的工具。所有这些建模方法都依赖于用于实时测量污染物浓度的传感器类型。对现有传感器技术的回顾表明，没有完美的传感器，但该领域的工作强度在不久的将来会提供有前景的结果。本研究的主要目标是将神经网络建模从室外扩展到室内源位置的识别，使这项技术适用于建筑物室内环境。开发的神经网络污染物源定位器还用于优化污染物浓度传感器的数量和分配，以实时预测室内污染物源位置。这种预测应该在接收到实时污染物浓度传感器数据后的几秒钟内进行。为了进行神经网络训练，一个多区域程序为测试建筑物中已知源位置提供了污染物浓度分布。经过训练的网络根据不同建筑物区域的测量浓度输出指示污染物源位置的信息。基于真实建筑物布局和实验数据的验证案例证明了该方法识别污染物源位置的能力。未来的研究意图集中在与真实传感器网络的集成以及对更复杂的污染场景的模型改进上。
