SBIR Phase I: Internet of Things (IoT)-Enabled Smart Filter

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是开发一种新的空气过滤器监测解决方案，这将对公共卫生，环境和美国经济产生积极影响。该技术基于使用光电传感器直接测量过滤器状态、使用传感器数据精确确定过滤器污染状况的智能信号处理算法以及用于控制和通信的物联网（IoT）。根据世界卫生组织（WHO）的数据，“环境空气污染每年造成约300万人死亡。世界上大约90%的人口暴露于超过世卫组织限制的水平。“虽然空气过滤技术本身无法解决环境空气污染的严重问题，但它必须成为全面解决方案的一个组成部分。通过数据驱动的决策来消除过早更换过滤器并降低成本，所提出的技术将推动高质量过滤器的使用更加普遍，从而改善公共健康。随着1.5亿个供暖、通风和空调（HVAC）系统的运行，以及每季度更换一次过滤器，估计每年有6亿个过滤器被制造和丢弃。减少50%的过滤器废物将对环境产生显著的积极影响，因为减少了制造和废物。这个小型企业创新研究（SBIR）第一阶段项目将利用光电传感器的准确性，通过传感透过过滤器的光来检测过滤器堵塞的程度。看似简单的解决方案背后是一系列复杂的技术挑战。由于包括褶皱和框架障碍物的不均匀结构，传感器数据固有地有噪声。软件控制的执行器将传感器放置在过滤器的前面，并从过滤器的多个位置获取数据。将开发一种智能算法，以从数据集的分析中提取参数，该参数将是美国供暖、制冷和空调工程师协会标准中定义的颗粒尺寸去除效率的准确代表，而该标准又有望成为真实过滤器年龄的足够准确的指标。使用物联网功能，传感器数据将在云中收集，智能算法和控制软件将存储在云中。最终目标是通过将参数与阈值参数进行比较来确定过滤器更换的最佳点，该阈值参数是根据室内空气质量要求预先确定的最大颗粒尺寸去除效率和气流阻力得出的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the development of a novel solution for air filter monitoring that will have a positive impact on public health, the environment, and the US economy. The technology is based upon the direct measurement of the filter status using photosensors, smart signal processing algorithms for accurate filter soiling condition determination using the sensor data, and the Internet of Things (IoT) for control and communication. According to the World Health Organization (WHO), "Ambient air pollution kills about 3 million people annually... About 90 percent of the world's population is exposed to levels exceeding WHO limits." While air-filtration technology alone cannot solve the overwhelming problem of ambient air pollution, it must be an integral part of a comprehensive solution. With data driven decision making to eliminate premature filter replacement and to reduce costs, the proposed technology will propel the usage of high-quality filters more ubiquitously, leading to enhanced public health. With 150 million heating, ventilation and air conditioning (HVAC) systems in operation, and quarterly filter replacement, an estimated 600 million filters are manufactured and thrown away every year. A reduction of 50% of the filter waste will have a significant positive impact on the environment because of reduced manufacturing and waste.This Small Business Innovation Research (SBIR) Phase I project will leverage the accuracy of photosensors in detecting the degree of filter blockage by sensing transmitted light through the filter. Underlying the seemingly straightforward solution is a set of complex technical challenges. Due to the uneven structure including pleats and frame obstructions, the sensor data are inherently noisy. A software-controlled actuator will place the sensor in front of the filter and take data from multiple locations of the filter. A smart algorithm will be developed to extract a parameter from the analysis of the data set that would be an accurate proxy for the particle size removal efficiency defined in the American Society of Heating, Refrigerating and Air-Conditioning Engineers standard which in turn is expected to be a sufficiently accurate indicator of the true filter age. Using the IoT capability, the sensor data will be collected in the cloud, where the smart algorithm and control software will be stored. The final objective is to determine the optimum point for filter replacement by comparing the parameter with a threshold parameter derived from a predetermined maximum particle size removal efficiency and airflow resistance based on indoor air quality requirements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.