Project: Autonomous Electrochemical Gas Detection Microsystem for Mine Safety

 

PI: Andrew Mason, Department of ECE, Michigan State University

PI: Xiangjun Zeng, Department of Chemistry, Oakland University

Co-PI: Rong Jin, Department of CSE, Michigan State University

 

Abstract:

Despite continued safety improvements and increased regulations, underground mines remain a very dangerous work environment, as evident from recent disasters at the Sago (2006), Darby (2006), and Crandall Canyon (2007) mines. As recommended by the Mine Safety Technology and Training Commission, new, cost-effective technologies are needed to enhance the monitoring and communication within mines. We propose to develop key sensor, instrumentation and data analysis technologies that will be integrated to form a miniaturized intelligent multi-gas monitoring system that is tailored to the needs and challenges of mine safety applications. The proposed electrochemical sensor array module will be capable of measuring concentrations of several gases to predict and prevent fires and explosions (CH4, H2, CO, CO2, O2) and to limit exposure of workers to hazardous exhaust gases (NO, NO2, SO2). Offering significant advantages over existing commercial gas sensors, the proposed sensor array will be implemented within a compact instrumentation system that will provide intelligent control and data analysis to enhance mine safety and permit the system to be readily deployed with existing mine infrastructure or recently developed wireless handsets for mine communication networks. An operational model of the proposed system will be implemented and characterized in a multi-gas mine-like environment. To realize the proposed system, major innovations in three diverse areas will be synergistically combined within the following specific aims: 1) Develop and characterize a miniaturized electrochemical sensor array for detection and quantification of multiple mine gasses, 2) Design and optimize compact electrochemical instrumentation electronics and intelligent algorithms for autonomous operation, 3) Integrate and characterize a model multi-gas electrochemical microsystem for mine safety monitoring and hazardous condition prediction. The first innovation is an electrochemical sensor approach in which room-temperature ionic liquids and conductive polymer membranes will be developed for detection of mine gases. The second innovation is an instrumentation chip design that implements multiple electrochemical measurement techniques to support an array of sensors and enable a very compact, low power microsystem implementation of the multi-gas monitoring system. The final innovation is a highly efficient sensor array data analysis algorithm that enables concentrations of specific gases to be accurately measured within a multi-gas environment and provides predictive estimates of conditions that merit alerts for fire, explosion, or health hazards. Together, these innovations enable a sensor array microsystem with excellent selectivity and low limits of detection, using a robust platform that is inherently resistant to common mine interferents. The inexpensive, compact, portable sensor system could be widely distributed throughout a mine for long term monitoring and rapid determination of safety alerts.

Students

  1. Jingfeng Yi

 

Project Goal:

 

To realize the proposed system, major innovations in three diverse areas will be synergistically combined within the following specific aims: 1) Develop and characterize a miniaturized electrochemical sensor array for detection and quantification of multiple mine gasses, 2) Design and optimize compact electrochemical instrumentation electronics and intelligent algorithms for autonomous operation, 3) Integrate and characterize a model multi-gas electrochemical microsystem for mine safety monitoring and hazardous condition prediction.

 

Publications

  1. Y. Yang ,J. Yi, R. Jin and Andrew J. Mason, Power-Error Analysis of Sensor Array Regression Algorithms for Gas Mixture Quantification in Low-Power Microsystems, IEEE SENSORS 2013