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DOE Funds R&D to Improve Coal-fired Power Plant Efficiency

The U.S. Department of Energy’s (DOE) Office of Fossil Energy (FE) chose 17 projects to receive roughly $39 million in federal funding for cost-shared research and development related to coal-fired power plants.

The DOE selected the projects as part of its Transformative Power Generation Program and Crosscutting Research Program. The projects are expected to develop technologies that improve the overall performance, reliability and flexibility of coal-fired power plants. The research will support the DOE’s goal to improve the average modeled efficiency (i.e., heat rate) of a typical plant in the existing fleet by 5% (from the 2017 baseline of 31%) by the end of fiscal year 2020.

The projects fall under three areas of interest: sensors, diagnostics and controls to improve prediction, performance and reliability; power plant component improvement; and data analytics driven controls.

The R&D projects being funded by the DOE include the following.

Test and validate distributed coaxial cable sensors for in situ condition monitoring of coal-fired boiler tubes

Clemson University will test and validate (through the planned field tests in an industrial-scale test facility and an operational utility plant) novel, low-cost, distributed high-temperature, stainless-steel/ceramic coaxial cable sensors, installation methods, instrumentation and data analytics for in situ monitoring of the health status of boiler tubes in existing coal-fired power plants.

Demonstration of multi-gamma-based sensor technology for as-fired coal property measurement

Microbeam Technologies (Grand Forks, North Dakota) will demonstrate a smart sensor that measures coal properties at the point of injection into burners at a full-scale power plant. Accurate and precise coal property information at the burner provides the opportunity to adjust burner parameters to better follow changing load conditions, optimize flame stability and decrease nitrogen oxide formation at each burner.

Deployment of dynamic neural network optimization to minimize heat rate during ramping for coal power plants

The University of Utah will develop and deploy dynamic neural network optimization (D-NNO) technology to minimize heat rate for coal power plants. The project goal is to improve cumulative heat rate by at least 5% relative to unoptimized operation and to produce a commercial D-NNO product that can be readily applied.

Transient efficiency, flexibility and reliability optimization of coal-fired power plants

A GE Company, GE Research aims to estimate then optimize heat rate for improved efficiency at part load, base load and during transients; increase the ramp rates from part load to base load for supporting the power grid against ever-increasing intermittent energy sources; and determine the health of the major components of coal-fired power plants for improved reliability. GE will achieve these goals via utilization of their coal-fired power plant model library, developing controllers that predict equipment response as a function of both operating point and transient loading.

Elimination of steam side scaling on grade 91 steel, improving efficiency, reliability and flexibility of existing fossil-fired power plants

Applied Thermal Coatings (Chattanooga, Tennessee) aims to significantly improve the reliability and efficiency of existing coal-fired power plants under flexible operating conditions by deploying a technology to modify the surface chemistry of creep-strength-enhanced steel tubing to substantially improve its steam-side oxidation resistance at a cost and scale that enables its ready acceptance for use by the power generation industry.

Mitigation of aerosol impacts on ash deposition and emissions from coal combustion

Barr Engineering Company (Minneapolis) will demonstrate the effectiveness of tailored sorbents in mitigating fouling and slagging; develop a benchmark/screening tool for identifying low-cost sorbents; and conduct a techno-economic assessment of the sorbent technology, including a pathway to commercialization.

Concrete thermal energy storage enabling flexible operation without coal plant cycling

The Electric Power Research Institute (Charlotte, North Carolina) will design, construct and test a pilot-scale concrete thermal energy storage system (CTES) to demonstrate the energy storage potential of the technology when applied to coal-fired power units. The project aims to demonstrate that a CTES system can be integrated with a coal power plant to enable low-cost energy storage that will eliminate the need for excessive operational flexibility and ultimately improve the profitability of the plant.

Plasma ignition and combustion stabilization technology to improve flexible operation, reliability and economics of an existing coal-fired boiler

GE Steam Power aims to demonstrate improved power plant reliability, flexibility and economics at PacifiCorp Hunter in a field demonstration of the advanced new high-efficiency alternating-current plasma technology. The objective of this program is to advance the plasma ignition technology to be a fully integrated and a field proven system, so it can then be made commercially available for other coal-fired power plants.

Investigations of technologies to improve condenser heat transfer and performance in a relevant coal-fired power plant environment

The Electric Power Research Institute will study and demonstrate the effectiveness of modifications that may enhance the performance of heat exchanger tubes for coal-fired power plant applications. Improvements in heat transfer effectiveness could result in power plant condensers having increased efficiency, reliability and flexibility.

Flexible coal power plant operation with thermal energy storage utilizing thermosiphons and cementitious materials

Lehigh University will develop an optimized prototype of a solid media thermal energy storage concept for thermal management applications in coal-fired power plants. The project will involve design, engineering, optimization and testing of the concept at laboratory- and at prototype-scale at a coal-fired power plant.

Anti-biofouling surface treatments for improved condenser performance for coal-based power plants

The Research Triangle Institute (Research Triangle Park, North Carolina) will design and engineer novel surface treatments and secondarily applied remediation components to mitigate biofilm growth on condenser tube surfaces used in coal-fueled power plants. Such modified surfaces can potentially disrupt the landscape for emerging anti-biofouling technologies through the creation of surface environments that interfere with abilities of bacteria to sense and respond to their environment, thereby inhibiting biofilm production and surface attachment.

Environmental validation of materials and design concepts to enable operational flexibility of existing coal power plants

Siemens Corporation, Corporate Technology (Charlotte) will develop a holistic approach for demonstration of multiple operational methodologies and improved materials capability versus baseline to improve efficiency, reliability and flexibility of existing coal-based power plants. The proposed technical approach is focused on performance of improved materials/plant operations on small-scale pilot plant facilities at Cranfield University for power plant component improvements.

Ash fouling free regenerative air preheater for deep cyclic operation

The University of Kentucky aims to develop a self-cleaning, ash fouling free air preheater to increase the capacity of a coal-fired power plant for load following. Increased use of alternative energy sources presents a challenge to control thermal efficiency. The proposed unit offers a solution to this, especially during deep cyclic operation and is transformative from the state-of-the-art regenerative heater with either hot-air recycling or a hot water recirculation system.

Hybrid analytics solution to improve coal power plant operations

Expert Microsystem Inc. (Orangevale, California) will develop, demonstrate and commercialize a novel approach to improve coal-fired power plants’ ability to follow loads and handle transient behavior by integrating two proven real-time monitoring techniques. The hybrid analytics approach integrates into a single, integrated solution, an established, advanced data-driven analytics solution that includes artificial intelligence, advanced pattern recognition and machine-learning techniques and a well-proven, first-principle thermal heat balance model solution.

Generation plant cost of operations and cycle optimization model — The National Rural Electric Cooperative Association will develop a tool to estimate the costs of cycling boilers in large coal plants so that coal generators can be fairly considered and efficiently operated as part of a generation and dispatch strategy. The Generation Plant Cost of Operations and Cycle Optimization Model will be refined and integrated with one or more dispatch and generation planning models through an application programming interface.

Boiler health monitoring using a hybrid first principles-artificial intelligence model — West Virginia University Research Corporation seeks to develop methodologies and algorithms to accomplish a hybrid first-principles-AI model of the PC boiler; a physics-based approach to material damage informed by ex-service component evaluation; an online health-monitoring framework that synergistically leverages the hybrid models and plant measurements to provide the spatial and temporal profile of key transport variables and characteristic measures for plant health; and a field implementation and demonstration at Southern Company’s Plant Barry.

Deep analysis net with causal embedding for coal-fired power plant fault detection and diagnosis

General Electric Company, GE Research will develop a novel end-to-end trainable artificial intelligence-based multivariate time series learning system for flexible and scalable coal power plant fault detection and root cause analysis.

DW Keefer's picture

Thank DW for the Post!

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Chavdar Azarov's picture
Chavdar Azarov on Jun 18, 2019 4:43 pm GMT

The average efficiency of a TPP is less than 40%.

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