Work

Multi-Factor Carbon Emissions Prediction in Coal-Fired Power Plants: A Machine Learning Approach for Carbon Footprint Management

Year: 2025

Type: article

Abstract: In coal-fired power plants, accurately accounting for carbon footprints is crucial for reducing greenhouse gas emissions and achieving sustainability goals. Life cycle assessment (LCA) is a comprehensive approach that expands the scope of carbon accounting, enabling the calculation of carbon emission data. However, the unclear boundary definition and incomplete data types often lead to insufficient accuracy in model calculations and predictive performance. Herein, we developed machine learning models to predict carbon emissions in a 1000 MW coal-fired power plant. The ElasticNet modeling approach demonstrated exceptional predictive accuracy (R2 = 0.9514; MAE = 435.42 metric tons CO2). Coal combustion constituted the predominant source of greenhouse gas emissions, with quarterly emissions reaching 1.63 million metric tons in Q1 and 1.11 million metric tons in Q3. Emission intensity exhibited remarkable stability across operational load ranges (1.0–1.1 kg/MWh). Notably, under high-load conditions (>70%), low-calorific-value coal generated marginally higher specific emissions (1.11 kg/MWh) compared to high-calorific-value coal (1.05 kg/MWh). The findings provide rational strategies for optimizing coal procurement strategies and environmental control measures, thereby facilitating an optimal balance between operational efficiency and environmental stewardship.

Source: Energies

DOI: 10.3390/en18071715

Authors: Xiaopan Liu, Haonan Yu, Hanzi Liu, Zhiqiang Sun

Institution: Central South University

Language: English

Topic: Environmental Impact and Sustainability

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137