Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

• Published in: Applied sciences Vol. 14; no. 18; p. 8442
• Main Authors: Zuo, Zitu, Niu, Yongjie, Li, Jiale, Fu, Hongpeng, Zhou, Mengjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Artificial intelligence; carbon dioxide gas; Climate change; Electric power-plants; Emissions (Pollution); Energy consumption; Energy minerals; Environmental law; Fossil fuels; Fuel industry; Greenhouse gases; Laws, regulations and rules; Machine learning; nitrogen oxides; Power plants; predictive model; Germany

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the app...