Development of novel dynamic machine learning-based optimization of a coal-fired power plant
Journal article, 2022

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.

Particle swarm optimization

Long short-term memory

Dynamic optimization

Machine learning

Power plant

Author

Landen D. Blackburn

University of Utah

Jacob F. Tuttle

Griffin Open Systems

Klas Andersson

University of Utah

Chalmers, Space, Earth and Environment, Energy Technology

Andrew Fry

Brigham Young University

Kody M. Powell

University of Utah

Computers and Chemical Engineering

0098-1354 (ISSN)

Vol. 163 107848

Subject Categories

Telecommunications

Communication Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.compchemeng.2022.107848

More information

Latest update

7/4/2024 6