scholarly journals Air/gas system dynamics of fossil fuel power plants. Volume 3. Experimental pressure test data of a 500-MW unit and of a 125-MW unit. Final report

1980 ◽  
Author(s):  
F. Goldschmied ◽  
D. Wormley ◽  
D. Rowell ◽  
J. Brown, Jr.
Keyword(s):  
System Dynamics ◽  
Test Data ◽  
Power Plants ◽  
Fossil Fuel ◽  
Final Report ◽  
Gas System ◽  
Pressure Test ◽  
Fossil Fuel Power Plants ◽  
Experimental Pressure

Boiler Implosion Control in Fossil Fuel Power Plants

1985 ◽  
Vol 107 (4) ◽  
pp. 267-269 ◽  
Author(s):  
S. Z. Wu ◽  
D. N. Wormley ◽  
D. Rowell ◽  
P. Griffith
Keyword(s):  
Power Plants ◽  
Fossil Fuel ◽  
Companion Paper ◽  
Simulation Techniques ◽  
Power Plant Boiler ◽  
Induced Draft Fan ◽  
Fossil Fuel Power Plants ◽  
Computer Simulation Techniques ◽  
Plant Boiler ◽  
Furnace Pressure

An evaluation of systems for control of fossil fuel power plant boiler and stack implosions has been performed using computer simulation techniques described in a companion paper. The simulations have shown that forced and induced draft fan control systems and induced draft fan bypass systems reduce the furnace pressure excursions significantly following a main fuel trip. The limitations of these systems are associated with actuator range and response time and stack pressure excursions during control actions. Preliminary study suggests that an alternative control solution may be achieved by discharging steam into the furnace after a fuel trip.

scholarly journals Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

2019 ◽  
Vol 11 (9) ◽  
pp. 1117 ◽  
Author(s):  
Haopeng Zhang ◽  
Qin Deng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Power Plants ◽  
North China ◽  
Fossil Fuel ◽  
Environment Monitoring ◽  
Remote Sensing Images ◽  
Plant Monitoring ◽  
Working Status ◽  
Fossil Fuel Power Plants

The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

Development of an Innovative Inspection Tool for Superheater Tubes in Fossil Fuel Power Plants

2021 ◽  
Vol 79 (7) ◽  
pp. 728-738
Author(s):  
Caique Lara ◽  
Julie Villamil ◽  
Anthony Abrahao ◽  
Aparna Aravelli ◽  
Guilherme Daldegan ◽  
...  
Keyword(s):  
Power Plants ◽  
Fossil Fuel ◽  
Small Diameter ◽  
Environmental Sensors ◽  
Multiple Components ◽  
Coiled Structure ◽  
Thickness Measurements ◽  
Fossil Fuel Power Plants ◽  
External Inspection ◽  
Modular Nature

Fossil fuel power plants are complex systems containing multiple components that require periodic health monitoring. Failures in these systems can lead to increased downtime for the plant, reduction of power, and significant cost for repairs. Inspections of the plant’s superheater tubes are typically manual, laborious, and extremely time-consuming. This is due to their small diameter size (between 1.3 and 7.6 cm) and the coiled structure of the tubing. In addition, the tubes are often stacked close to each other, limiting access for external inspection. This paper presents the development and testing of an electrically powered pipe crawler that can navigate inside 5 cm diameter tubes and provide an assessment of their health. The crawler utilizes peristaltic motion within the tubes via interconnected modules for gripping and extending. The modular nature of the system allows it to traverse through straight sections and multiple 90° and 180° bends. Additional modules in the system include an ultrasonic sensor for tube thickness measurements, as well as environmental sensors, a light detecting and ranging (LiDAR) sensor, and camera. These modules utilize a gear system that allows for 360° rotation and provides a means to inspect the entire internal circumference of the tubes.

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