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THERMAL SCIENCE
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DYNAMIC MODELING RESEARCH ON THERMAL EFFICIENCY OF SUPERCRITICAL DOWN-FIRED BOILER
ABSTRACT
To accurately predict the coal-fired generating units’ thermal efficiency under deep peak shaving conditions, a dynamic prediction method of boiler thermal efficiency is proposed. Determine the auxiliary variables affecting boiler thermal efficiency by analyzing the proportions of heat loss in the anti-balance method, and using the random forest algorithm to carry out supervised dimensionality reduction of auxiliary variables affecting the thermal efficiency. On this basis, the convolutional neural network (CNN) with dynamic modelling function is selected as the infrastructure, and to ensure the lightweight model, the cross-channel communication unit is inserted into the conventional CNN with only three convolutional layers to solve the problem of no interaction of feature maps in the same convolutional layer, and a boiler thermal efficiency predictive model based on the cross-channel communication CNN (C3-CNN) is constructed. Simulation experiments were conducted on the actual operation data for a 600 MW boiler, the results show that the developed method is equally suitable for both transient and steady-state conditions.
KEYWORDS
PAPER SUBMITTED: 2025-08-16
PAPER REVISED: 2025-09-03
PAPER ACCEPTED: 2025-09-04
PUBLISHED ONLINE: 2025-11-01
DOI REFERENCE: https://doi.org/10.2298/TSCI250816179H
CITATION EXPORT: view in browser or download as text file
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© 2026 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence