Development of novel dynamic machine learning-based optimization of a coal-fired power plant. (July 2022) - Blackburn, Landen D.; Tuttle, Jacob F.; Andersson, Klas; Fry, Andrew; Powell, Kody M.
Highlights: Demonstrates a novel control algorithm for a coal power plant under load-following. Dynamic optimization using long short-term memory with particle swarm optimization. Machine learning-based optimization of coal-fired power plant. Increased efficiency in response to more intermittent renewable penetration. Comparison using simulated physics-based coal-fired power plant model. Abstract: The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coal-fired boilers now routinely ramp up and down. The current state-of-the-art operation for such boilers is to apply steady-state, neural network-based optimization to make control decisions in real-time, and this work demonstrates the feasibility of extending this to dynamic, neural network-based optimization using a long short-term memory neural network. A simplified numerical simulation of a t-fired coal boiler and supporting equipment is used to represent a real plant subjected to both steady-state, neural network-based optimization and dynamic, neural network-based optimization. Using the same intervals and a particle swarm optimization algorithm, the dynamic optimization outperforms the steady-state optimization and realizes up to 4.58% improvement in thermal efficiency. Dynamic optimization with a long short-term memory neural network is shown to both be feasible and beneficial for operation of a coal-fired boiler under changing load.