Powers of Perception: The State of the Art and Future of Sensors in Coal Power Plants

By Toby Lockwood
Technical Author, IEA Clean Coal Centre

Coal plant operators are increasingly constrained by a wide range of conflicting objectives, as they seek to maximize efficiency, profit, availability, and plant lifetime, while minimizing emissions and water consumption. The best set of operational parameters required to satisfy these demands can also be subject to constant change, as growing grid-connected capacities of intermittent wind and solar power oblige thermal power stations to ramp their output, and economic and environmental incentives encourage switching of coal type or biomass co-firing. To face these challenges, automation and more intelligent control systems able to optimize plant operation faster and more effectively than human operators are in increasing use; yet such systems rely on sensors to provide accurate data from the processes they control. Whereas in the past much of the operational data available to coal-fired power plant operators derived from imperfect, periodic measurements used to set long-term operating parameters, advances in sensor technologies over the last decade are now giving control systems access to a continuous stream of real-time data from previously inaccessible regions of the plant. This allows for human operators or the automated control system to take action based on considerably more information. As well, online sensors can also play an important role in monitoring the condition and performance of plant components and identifying when maintenance is required. This is particularly important given the unfamiliar and challenging operating regimes associated with frequent load following or non-design fuels.


The fundamental control problem faced by pulverized coal-fired power plants is that of achieving uniform and optimized combustion throughout the furnace by optimizing the fuel-to-air ratio. While a certain amount of excess air is required for complete coal combustion, excessive air leads to increased NOX formation and a reduction in boiler efficiency due to increased heat loss in the larger volume of flue gas. In the absence of detailed data on the combustion process, many plants operate with excessive levels of air in order to ensure complete combustion and avoid corrosive, reducing conditions in the boiler. Such plants, said to be operating in a comfort zone rather than an optimum zone, incur an efficiency penalty and an increased demand on downstream NOX abatement (see Figure 1).

FIGURE 1. Optimizing the air-fuel ratio can increase efficiency and minimize NOx formation (FEGT, furnace exit gas temperature; LOI, loss on ignition)1

The overall fuel-to-air ratio is typically set using one or two oxygen sensors at the economizer exit to measure excess air levels, together with carbon monoxide (CO) monitoring at the stack. Rising CO levels are a sensitive indicator of incomplete combustion which can be countered by increasing the excess air. However, in large furnaces with multiple burners, regions of locally poor combustion can easily occur, leading to localized flue gas columns with high CO or NOX, which are difficult to measure. As a result, greater volumes of air than are necessary may be fed to the boiler to eliminate high CO levels generated by a single burner.

As many of these combustion optimization problems ultimately derive from an uneven distribution of coal and air between burners, online mass flow sensors are increasingly taking the place of the periodic, isokinetic sampling measurements previously used to monitor and tune coal distribution between pipes. With a variety of pipe lengths and geometries typically used to convey coal from mills to burners, imbalances in coal flow are common, but achieving accurate flow data in these turbulent and erosive conditions is challenging.2

Several commercial technologies are now available that use a variety of different measurement principles, including microwaves, light, and the electrostatic charge developed by the coal particles.2 For example, Greenbank’s PFMaster consists of two electrode rings which sit flush with the coal pipe for minimal intrusion, and correlate unique charge signals from the coal particles passing each electrode to determine their time-of-flight and velocity (Figure 2).1 Together with the magnitude of the charge and knowledge of the total coal feed rate, the distribution of mass flow over each pipe can be determined and used to “balance” the fuel-to-air ratio across the furnace. German manufacturer Promecon’s Mecontrol sensor combines a similar cross-correlation principle with a microwave-based technique to obtain coal density and, thus, an absolute value of mass flow, whereas EUcoalflow from EUtech uses an entirely microwave-based method to measure the coal particle velocity.

FIGURE 2. Measuring coal particle velocity by cross-correlation of electrostatic charge signals1

Installation of these devices, often in combination with specialized actuators for evenly distributing coal between pipes, has been shown to lead to significant efficiency improvements and NOX reductions associated with minimizing the excess air required for complete combustion. For example, installation of PFMaster at Datong power plant in China resulted in a boiler efficiency increase of up to 0.8% points and a 25% reduction in NOX emissions. At Yeongheung plant in South Korea, Mecontrol was used to effectively ease the switch to a lower grade sub-bituminous coal that was previously creating poor combustion and operational issues such as fan stalls.2

Optimizing coal flow can still be problematic if coal particles are too coarse to approach fluid-like flow behavior, either as a result of incorrect pulverizer mill parameters or grinding elements in poor condition. A range of complementary sensors is therefore also available for online measurement of particle size distribution, usually based on imaging with lasers or white light followed by rapid image analysis. The information from these sensors can be used to adjust and calibrate coal flow data, control the mill classifiers that regulate particle size, or alert operators to poorly performing mills.

The difficulty of obtaining data from the hostile environment of the furnace previously reduced it to the status of a “black box”, into which fuel and air are fed and from which only the cooled combustion products can be analyzed. Advances in sensor technologies now allow operators to “see” inside the furnace itself and map variations in temperature and gas concentrations in real time, providing an invaluable tool for balancing and optimizing combustion and avoiding problems such as excessive slagging. Installed at over 60 sites, the ZoloBOSS system from Zolo Technologies employs a grid of infrared lasers that crisscross the furnace, using a technique known as tunable diode laser absorption spectroscopy to map concentrations of NOX, CO, and O2, as well as temperature data derived from water absorption peaks (Figure 3).3 This spatial resolution allows localized regions of CO or NOX from poorly tuned burners to be easily identified and coal and air flows to be redistributed accordingly, either as part of a manual tuning process at steady state or by feeding data to an automated combustion optimization program for dynamic operation. The accurate furnace temperature data can also be usefully applied to intelligent soot blowing systems, which often rely on basic measurements of furnace exit gas temperature taken at a single point by optical pyrometers.

FIGURE 3. The ZoloBOSS laser system produces maps of gas and temperature distribution in the furnace.3


The value of advanced sensor technologies for upgrading the fossil fuel fleet has been fully recognized by the U.S. Department of Energy (DOE), whose Crosscutting Technology Research program funds research into a host of novel sensor concepts under development by academic institutes and the private sector.4 This program has particularly focused on sensors which will be able to survive in the even harsher conditions likely to be encountered in future coal plants, such as the high-temperature, reducing atmospheres of gasifiers used in integrated gasification combined-cycle plants or the elevated advanced ultra-supercritical steam temperatures.

Many of the sensors being developed are miniaturized, solid state devices which can be packaged and deployed in large numbers to maximize data flow from the process. However, traditional silicon-based chips cannot withstand the temperatures of over 1000°C encountered in coal furnaces, gasifiers, and gas turbines. Novel materials such as high-temperature ceramics or silicon alloys are instead being employed for the fabrication of more robust devices, with new gas sensor designs even making use of high-surface-area nanomaterials to enhance their performance.1

A key component of high-temperature sensor research in the U.S. and elsewhere is the use of optical devices which use light instead of electrons as their medium for sensing and transmitting information. Not only can optical fibers be made from materials with high thermal stability, such as sapphire, they are immune to signal interference from the widespread electromagnetic noise in power plants, and miniaturized devices can be created by engineering or coating short sections of fibers to modulate light according to the temperature, pressure, or chemistry of their environment.1 Using a micromachined sapphire tip on an optical fiber to act as a miniature interferometer, a commercial optical sensor from Oxsensis (UK) can achieve integrated temperature and pressure sensing up to 1000ᵒC, and has been applied to condition monitoring and control of gas turbines.5 Optical fibers can also be interrogated to yield information on the environment along their entire length. Known as distributed sensing, this is widely used for monitoring large structures in civil engineering or characterizing oil wells. There are also high-temperature versions of these devices being developed for the power industry. A novel concept being investigated by the University of Massachusetts is to surround a coal furnace with optical fibers engineered to both generate and detect sound waves, allowing the temperature profile of the whole space to be mapped out in 3D by acoustic pyrometry (Figure 4).6

FIGURE 4. The optical fiber-based acoustic pyrometry concept developed by the University of Massachusetts6

To protect these sensors without the need for costly packaging, and to bring them closer to the processes they are used to monitor, researchers are also attempting to embed them into power plant components, such as steam pipes and turbine blades, using additive manufacturing techniques (Figure 5). Metal parts can be built up using targeted lasers to selectively fuse together layers of a powder or foil sheets; ceramic parts, such as refractory material, can be manufactured by extrusion and solidification of a precursor paste, allowing the optical fiber sensor to be placed directly within the component at the appropriate stage. As part of the EU-funded project OXIGEN, researchers at Herriott-Watt University in the UK incorporated optical fiber sensors within turbine blades to produce a “smart part” able to report on its strain and temperature and thus preempt material failure.7 Several U.S. universities are collaborating to develop boiler tubes, turbine blades, and refractory materials with embedded optical fibers capable of distributed strain sensing. A key challenge for these projects is achieving good adhesion between the sensor and the host material.8

FIGURE 5. Schematics of a boiler tube and gas turbine pre-mixer with embedded sensors9

These developments point toward an emerging future for fossil-fueled power plants, wherein an army of sensors provides a constant stream of data on the performance of each process and the condition of every component. How to best handle and exploit this potential flood of data is another challenge for researchers, which may require a fundamental rethink of power plant control systems. “Smarter” sensors with embedded processing power can be combined with wireless communication to create highly interconnected networks, able to take control decisions without the higher-level supervision found in traditional, hierarchical systems. This kind of distributed intelligence network can begin to mimic the emergent behavior found in biological systems, potentially offering greater capability for adapting and meeting competing plant objectives, and could be key to effectively managing the larger, more complex power systems of the future.9


As the growing operational demands on modern power stations emphasize the need for more accurate, real-time data from all areas of the plant, sensors are coming to represent the cutting edge of coal power research. Rapid advances in novel technologies such as nanomaterials, optics, and additive manufacturing are outpacing the rate at which they can be implemented in commercial sensors. The industry is only beginning to exploit the potential benefits they can bring to process control and predictive maintenance. Although attention has often focused on the development of new materials to increase plant efficiencies and availability, advanced sensors can offer an effective and economic means of achieving the same goals. Given the size of the world’s coal power fleet, such incremental gains in efficiency and component lifetimes can represent significant reductions in emissions and huge financial savings. In fact, the U.S. DOE estimated potential yearly savings of over $350 million and 14 million tons of CO2 from control system upgrades in the U.S. alone.4 There is, therefore, every incentive for coal power to embrace the information age.


  1. Lockwood, T. (2015). Advanced sensors and smart controls for coal-fired power plant. CCC/251. London: IEA Clean Coal Centre.
  2. Wiatros-Motkya, M. (2016). Optimising fuel flow in pulverised coal and biomass-fired boilers. CCC/263. London: IEA Clean Coal Centre.
  3. Stewart, J., Thavamani, S., Spaeth, T., Sarma, A., & Kelly, B. (2012). Lowering coal-fired NOx through robust hybrid combustion optimization. Presented at POWER-GEN International Conference, 11–13 Dec., Orlando, FL.
  4. NETL. (2015). Sensors and controls. Available at: www.netl.doe.gov/research/coal/crosscutting/sensors-controls
  5. Hemsley, D. (2015). Fibre optic sensors for harsh environment applications. Presented at KTN Event: sensing challenges in extreme environments, 7 May, London, UK.
  6. Mullen, J. (2016). Distributed-fiber sensing systems for 3D combustion temperature field monitoring in coal-fired boilers using optically generated acoustic waves. Presented at: 2016 Crosscutting research and rare earth elements portfolios review meeting, 18–22 April, National Energy Technology Laboratory, Pittsburgh, PA. www.netl.doe.gov/File%20Library/Events/2016/crosscutting-ree/track-b-041816/Presentantion_20160418_1400B_FE0023031_UML.pdf
  7. Mathew, J., Havermann, D., MacPherson, W.N., Hand, D.P., Carter, R.M., & Maier, R.J. (2014). Strain isolated optical fibre sensors for high temperature applications. Presented at Photon14, 1–4 September, London, UK.
  8. Lin, Y. (2016). Investigation of “smart parts” with embedded sensors for energy system applications. Presented at 2016 Crosscutting research and rare earth elements portfolios review meeting, 18–22 April, National Energy Technology Laboratory, Pittsburgh, PA. www.netl.doe.gov/File%20Library/Events/2016/crosscutting-ree/track-b-041916/Presentantion_20160419_1630B_FE0012321_UTEP.pdf
  9. Romanosky, R. (2016). Crosscutting research—programme overview. Presented at 2016 Crosscutting research and rare earth elements portfolios review meeting, 18–22 April, National Energy Technology Laboratory, Pittsburgh, PA. www.netl.doe.gov/File%20Library/Events/2016/crosscutting-ree/Technology-Manager_Crosscutting-Overview_.pdf

The author can be reached at Toby.Lockwood@iea-coal.org


The content in Cornerstone does not necessarily reflect the views of the World Coal Association or its members.
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