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Title: Wireless microwave acoustic sensor system for condition monitoring in power plant environments

Abstract

This project successfully demonstrated novel wireless microwave acoustic temperature and pressure sensors that can be embedded into equipment and structures located in fossil fuel power plant environments to monitor the condition of components such as steam headers, re-heat lines, water walls, burner tubes, and power turbines. The wireless microwave acoustic sensor technology researched and developed through a collaborative partnership between the University of Maine and Environetix Technologies Corporation can provide a revolutionary impact in the power industry since it is anticipated that the wireless sensors will deliver reliable real-time sensing information in harsh power plant conditions that involve temperatures up to 1100oC and pressures up to 750 psi. The work involved the research and development of novel high temperature harsh environment thin film electrodes, piezoelectric smart microwave acoustic sensing elements, sensor encapsulation materials that were engineered to function over long times up to 1100oC, and a radio-frequency (RF) wireless interrogation electronics unit that are located both inside and outside the high temperature harsh environment. The UMaine / Environetix team have interacted with diverse power plant facilities, and identified as a testbed a local power generation facility, which burns municipal solid waste (MSW), the Penobscot Energy Recovery Company (PERC), Orrington, Maine.more » In this facility Environetix / UMaine successfully implemented and tested multiple wireless temperature sensor systems within the harsh-environment of the economizer chamber and at the boiler tubes, transferring the developed technology to the power plant environment to perform real-time sensor monitoring experiments under typical operating conditions, as initially targeted in the project. The wireless microwave acoustic sensor technology developed under this project for power plant applications offers several significant advantages including wireless, battery-free, maintenance-free operation, and operation in the harsh-environment of power plant equipment up to about 1100 oC. Their small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems that can be interrogated by a single RF unit. The outcomes of this project and technological transfer respond to a DOE analysis need, which indicated that if one percent efficiency in coal burning is achieved, an additional 2 gigawatt-hours of energy per year is generated and the resulting coal cost savings is $300 million per year, also accompanied by a reduction of more than 10 million metric tons of CO2 per year emitted into the atmosphere. Therefore, the developed harsh environment wireless microwave acoustic sensor technology and the technological transfer achievements that resulted from the execution of this project have significant impact for power plant equipment and systems and are well-positioned to contribute to the cost reduction in power generation, the increase in power plant efficiency, the improvement in maintenance, the reduction in down-time, and the decrease in environmental pollution. The technology is also in a position to be extended to address other types of high-temperature harsh-environment power plant and energy sector sensing needs.« less

Authors:
 [1]
  1. Univ. of Maine, Orno, ME (United States)
Publication Date:
Research Org.:
Univ. of Maine, Orno, ME (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
Contributing Org.:
Environetix Technologies Corporation
OSTI Identifier:
1406890
Report Number(s):
DOE-UMaine-FE0007379
DOE Contract Number:
FE0007379
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; High-Temperature Sensors; Harsh-Environment Sensors; Microwave Acoustics Technology; Wireless Sensors

Citation Formats

Pereira da Cunha, Mauricio. Wireless microwave acoustic sensor system for condition monitoring in power plant environments. United States: N. p., 2017. Web. doi:10.2172/1406890.
Pereira da Cunha, Mauricio. Wireless microwave acoustic sensor system for condition monitoring in power plant environments. United States. doi:10.2172/1406890.
Pereira da Cunha, Mauricio. Thu . "Wireless microwave acoustic sensor system for condition monitoring in power plant environments". United States. doi:10.2172/1406890. https://www.osti.gov/servlets/purl/1406890.
@article{osti_1406890,
title = {Wireless microwave acoustic sensor system for condition monitoring in power plant environments},
author = {Pereira da Cunha, Mauricio},
abstractNote = {This project successfully demonstrated novel wireless microwave acoustic temperature and pressure sensors that can be embedded into equipment and structures located in fossil fuel power plant environments to monitor the condition of components such as steam headers, re-heat lines, water walls, burner tubes, and power turbines. The wireless microwave acoustic sensor technology researched and developed through a collaborative partnership between the University of Maine and Environetix Technologies Corporation can provide a revolutionary impact in the power industry since it is anticipated that the wireless sensors will deliver reliable real-time sensing information in harsh power plant conditions that involve temperatures up to 1100oC and pressures up to 750 psi. The work involved the research and development of novel high temperature harsh environment thin film electrodes, piezoelectric smart microwave acoustic sensing elements, sensor encapsulation materials that were engineered to function over long times up to 1100oC, and a radio-frequency (RF) wireless interrogation electronics unit that are located both inside and outside the high temperature harsh environment. The UMaine / Environetix team have interacted with diverse power plant facilities, and identified as a testbed a local power generation facility, which burns municipal solid waste (MSW), the Penobscot Energy Recovery Company (PERC), Orrington, Maine. In this facility Environetix / UMaine successfully implemented and tested multiple wireless temperature sensor systems within the harsh-environment of the economizer chamber and at the boiler tubes, transferring the developed technology to the power plant environment to perform real-time sensor monitoring experiments under typical operating conditions, as initially targeted in the project. The wireless microwave acoustic sensor technology developed under this project for power plant applications offers several significant advantages including wireless, battery-free, maintenance-free operation, and operation in the harsh-environment of power plant equipment up to about 1100 oC. Their small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems that can be interrogated by a single RF unit. The outcomes of this project and technological transfer respond to a DOE analysis need, which indicated that if one percent efficiency in coal burning is achieved, an additional 2 gigawatt-hours of energy per year is generated and the resulting coal cost savings is $300 million per year, also accompanied by a reduction of more than 10 million metric tons of CO2 per year emitted into the atmosphere. Therefore, the developed harsh environment wireless microwave acoustic sensor technology and the technological transfer achievements that resulted from the execution of this project have significant impact for power plant equipment and systems and are well-positioned to contribute to the cost reduction in power generation, the increase in power plant efficiency, the improvement in maintenance, the reduction in down-time, and the decrease in environmental pollution. The technology is also in a position to be extended to address other types of high-temperature harsh-environment power plant and energy sector sensing needs.},
doi = {10.2172/1406890},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Mar 30 00:00:00 EDT 2017},
month = {Thu Mar 30 00:00:00 EDT 2017}
}

Technical Report:

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  • A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the currentmore » architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.« less
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