Development of novel dynamic machine learning-based optimization of a coal-fired power plant
Artikel i vetenskaplig tidskrift, 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

Författare

Landen D. Blackburn

University of Utah

Jacob F. Tuttle

Griffin Open Systems

Klas Andersson

University of Utah

Chalmers, Rymd-, geo- och miljövetenskap, Energiteknik

Andrew Fry

Brigham Young University

Kody M. Powell

University of Utah

Computers and Chemical Engineering

0098-1354 (ISSN)

Vol. 163 107848

Ämneskategorier

Telekommunikation

Kommunikationssystem

Annan elektroteknik och elektronik

DOI

10.1016/j.compchemeng.2022.107848

Mer information

Senast uppdaterat

2024-07-04