Date of Award

January 2023

Document Type


Degree Name

Doctor of Philosophy (PhD)


Energy Engineering

First Advisor

Stanley Olusegun Tomomewo


Fluctuations in feedstock properties significantly impact the reliability, efficiency, and emissions of energy conversion systems.The behavior of ash-forming impurities in solid feedstocks such as coal, biomass, petroleum coke, and municipal solid waste is a perennial challenge in energy generation. The transition to power plants with dynamic ramping capabilities as well as net-zero or negative carbon emissions is hampered in part by the impacts of ash-forming impurities on systems such as gasifiers, combustors, gas cleanup, and carbon capture systems. Many ash-related events that disrupt operations are caused by fluctuations in the properties of the feedstock. Online feedstock properties monitoring systems have experienced significant advancement, providing an opportunity to improve performance in operating these systems. The primary working hypothesis is stated as follows: Monitoring and controlling fluctuations in as-fired feedstock properties can enable energy conversion system performance optimization.

Efforts to prove or disprove the working hypothesis are broken down into four major areas: 1) monitoring and tracking fluctuations in feedstock properties; 2) understanding the impacts of feedstock properties on performance; 3) predicting plant performance based on tracked feedstock properties and understanding impacts; and 4) optimizing energy conversion system performance based on tracked feedstock properties and predicted plant performance.

In the first area, a feedstock properties tracking system was designed and implemented at a full-scale power plant. The system tracks fuel via an online analyzer through a fuel handling system to the burner. The tracking system was validated by comparing predicted as-fired fuel properties to laboratory measurements of fuel properties from periodic samples collected at the end of the feedstock handling system.

Second, the feedstock properties tracking system was deployed to understand the properties of ash deposits formed in the high-temperature regions of the convective pass at a commercial power plant under changing fuel conditions. Third, the tracking system was used to improve the ability of neural networks to predict flame intensity in a cyclone-fired burner. Finally, the feedstock properties tracking system was integrated into an online process control tool onsite at a commercial power plant that enables plant optimization.

This work ultimately demonstrates the use of an online feedstock properties monitoring and management system to optimize feedstock properties and plant performance in real-time. Visualization techniques and software were developed and deployed to indicate plant performance to actively control feedstock properties and improve plant operations. Operators use the software on a daily basis to sort and blend coals. The use of the tool is shown to improve plant performance predictions at a full-scale power plant. The work enables the use of automated feedstock properties control to improve the performance of energy conversion systems.

Available for download on Monday, August 25, 2025