Measurements of ash deposition rates were made between the secondary superheater and reheater sections of a 450 MW cyclone-fired lignite boiler as the operational load varied from 33 to 100%. Significant reductions in deposition rates with a decrease in operational load were observed. To uncover the causative mechanisms behind these observations, operational data from the power plant were used to carry out computational fluid dynamic (CFD) simulations of the boiler. After ascertaining that the gas temperatures and velocities at various sections within the boiler were being represented adequately, decoupled simulations of the ash deposition process on the deposit probe were carried out using a finely resolved boundary layer mesh. Fly ash particle size distribution (PSD) and its concentration for the decoupled calculations were determined from stand-alone cyclone barrel simulations. The ash partitioning (mass %) between the fly ash and slag was found to be ~50:50, which was in line with previous field observations, and it did not vary significantly across different cyclone loads. The predicted PSD of the deposit ash was concentrated in the size range 10–30 microns, which was in agreement with cross-sectional images of the deposit obtained from the measurements. At lower loads, sharp variations in the deposition rates were predicted in the gas temperature range 950–1150 K. The particle kinetic energy—particle viscosity-based capture methodology utilized in this study in conjunction with appropriate ash compositions, ash viscosity models and gas temperature estimates can help estimate slagging propensities at different loads reasonably well in these systems.

This work presents an investigation into the ability of recurrent neural networks (RNNs) to provide long term predictions of time series data generated by coal fired power plants. While there are numerous studies which have used artificial neural networks (ANNs) to predict coal plant parameters, to the authors’ knowledge these have almost entirely been restricted to predicting values at the next time step, and not farther into the future. Using a novel neuro-evolution strategy called Evolutionary eXploration of Augmenting Memory Models (EXAMM), we evolved RNNs with advanced memory cells to predict per-minute plant parameters and per-hour boiler parameters up to 8 hours into the future. These data sets were challenging prediction tasks as they involve spiking behavior in the parameters being predicted. While the evolved RNNs were able to successfully predict the spikes in the hourly data they did not perform very well in accurately predicting their severity. The per-minute data proved even more challenging as medium range predictions miscalculated the beginning and ending of spikes, and longer range predictions reverted to long term trends and ignored the spikes entirely. We hope this initial study will motivate further study into this highly challenging prediction problem. The use of fuel properties data generated by a new Coal Tracker Optimization (CTO) program was also investigated and this work shows that their use improved predictive ability of the evolved RNNs.

The Barr Engineering Co. (Barr) team that was comprised of the University of North Dakota, Microbeam Technologies Inc., Envergex LLC, and MLJ Consulting (collectively the Project Team) successfully demonstrated the ability to decrease ash deposition and reduce fine particles utilizing a sorbent-based aerosol mitigation technology in a 250-MW boiler firing alkali-rich (sodium, potassium) fuels. Fuels with high alkali concentration cause excessive fireside ash deposition on boiler heat transfer surfaces (slagging and fouling) and contribute to higher concentration of alkali-rich fine-ash particles (aerosols) that are difficult to capture in electrostatic precipitators. The project was funded by the US Department of Energy (DOE) National Energy Technology Laboratory, under Award Number DE-FE0031756, with co-funding from the North Dakota Industrial Commission, Otter Tail Power Coyote Station, Minnkota Power’s Milton R. Young Station, and North American Coal Corporation. This project was developed to test and validate technologies and methodologies that can improve the efficiency, reliability, and/or flexibility of individual power plant components in existing plants. The primary objective of this project was to validate a sorbent-based technology for mitigating fireside heat transfer surface fouling-related challenges in a full-scale coal-fired power generation facility, thereby, improving plant performance and reliability. Tasks included sorbent and coal characterization, labmore » combustor testing, two (2) separate field demonstration tests on a 250 MW unit, and techno-economic analysis (TEA). The demonstration confirmed several positive outcomes from implementing the sorbent injection technology: • Successful full-scale operation of sorbent injection for multiple days at an existing coal-fired power plant firing high-alkali-content coal • Up to 46% decrease in the sub-micron ash loadings during sorbent injection • Reduction in sodium (Na2O) concentration in the sub-micron ash ranging from 40-80% • Reduction in both high- and low-temperature convective pass fouling deposit growth and increased shedding, resulting in near-zero high-temperature silicate deposit accumulations • A decrease in the degree of sulfation, observed in low-temperature convective pass region deposits for the medium sorbent injection rates, contributed to lower growth rates and decreased sintering • Reduction of available alkali and sulfate (SO3) content of the fly ash, promoting the salability of the fly ash as a supplemental cementitious material • Improved boiler performance in several areas including boiler convective-pass cleanliness, reduced flue gas–side pressure drop, and lower back-end flue gas temperatures • A positive financial return for the implemented technology at a generic 500-MWe power plant • Development of a screening tool that the industry can use to predict sodium aerosol reduction performance with low-cost, locally available sorbents and its suitability for long-term application in boilers. « less