A Sensor Network System for Process Unit Emissions Monitoring
Citation:
Peng, W., D. Masner, L. Lin, A. Chernyshov, B. Kelly, M. Clausewitz, D. Cartwright, A. Narverud, K. Anderson, AND E. Thoma. A Sensor Network System for Process Unit Emissions Monitoring. Air and Waste Management Association Air Quality Measurements Methods and Technology Conference, Durham,NC, April 02 - 04, 2019.
Impact/Purpose:
Energy production operations, refineries, chemical plants, and other industries and waste facilities can emit air pollutants and odorous compounds from fugitive leaks, process malfunctions, and area sources that are hard to detect and manage. From the shared perspective of industries, regulators, and communities, improved understanding of stochastic industrial sources (SIS) can yield many benefits such as safer working environments, cost savings through reduced product loss, lower airshed impacts, and improved community relations. The emergence of lower-cost sensors and inverse modeling approaches, is enabling new cost-effective ways to detect and analyze SIS emissions. Under its next generation emissions measurement (NGEM) program, EPA is working with a range of partners to develop and test NGEM tools that can assist facilities in detection and management of sources. As described in the below abstract, the following product contributes to the general advancement and communication of NGEM concepts.
Description:
The following abstract is for presentation at the 2019 Air and Waste Management Association’s Air Quality Measurements and Methods Conference in Durham, NC, April 2-4, 2019. Leak detection and repair (LDAR) programs have relied heavily on EPA Method 21, which is labor intensive and performed on fixed monitoring schedules. The present innovation provides a sensor network-based emissions monitoring system that is configured to detect plumes of volatile organic compounds (VOC), or other gases of interest, within the boundaries of a facility. The monitoring system consists of strategically-placed sensor nodes, meteorological stations, and a data analytics and visualization platform powered by Molex’s mSyte™ software. The sensors are installed in fixed locations throughout the process unit and wirelessly communicate with the central data platform. The system analyzes data using custom gas dispersion models and site-specific algorithms. With appropriate sensor technology, the system has shown quick detection of VOC leaks as low as 1.5 grams per hour in controlled release studies. The system detects anomalous emissions, identifies the probable source location, and sends appropriate alerts and notifications. As part of a layered monitoring framework, optical gas imaging and other technologies are deployed to pinpoint and assess the actual emission source after notification and/or in supplementary form.