[HTML][HTML] Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants

Z Zuo, Y Niu, J Li, H Fu, M Zhou - Applied Sciences, 2024 - mdpi.com
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 …

[HTML][HTML] Digital innovation's contribution to sustainability transitions

T Mäkitie, J Hanson, S Damman, M Wardeberg - Technology in Society, 2023 - Elsevier
Digital innovation is increasingly mentioned as a potential key contributor to sustainability
transitions. However, there has been little theoretical discussion of this topic. In this …

Analysis of pollutant emission reduction in a coal power plant using renewable energy

GF Smaisim, AM Abed, H Alavi - International Journal of Low …, 2023 - academic.oup.com
The major and challengeable restriction facing coal power plants (CPPs) is the emission of
polluting gases caused by burning coal. Therefore, adopting the reasonable and practical …

A review on the application of machine learning for combustion in power generation applications

K Mohammadi, J Immonen, LD Blackburn… - Reviews in Chemical …, 2023 - degruyter.com
Although the world is shifting toward using more renewable energy resources, combustion
systems will still play an important role in the immediate future of global energy. To follow a …

Model predictive control of power plant cycling using Industry 4.0 infrastructure

D Kestering, S Agbleze, H Bispo, FV Lima - Digital Chemical Engineering, 2023 - Elsevier
This work involves the Industry 4.0 infrastructure developed at West Virginia University
(WVU) for process systems applications. This infrastructure emulates an interconnected …

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

E Gallup, T Gallup, K Powell - Computers & Chemical Engineering, 2023 - Elsevier
As neural networks are more frequently used to solve problems in science and engineering,
the methods used to incorporate scientific knowledge into these networks are becoming …

Parametric and heuristic optimization of multiple schemes with double-reheat ultra-supercritical steam power plants

I Opriș, VE Cenușă - Energy, 2023 - Elsevier
Improvement of energy conversion efficiency in modern steam thermal power plants and
minimization of the impact on the environment can be both achieved using ultra-supercritical …

[HTML][HTML] Scale-up of oxidative desulfurization for sour diesel fuel: Modeling, simulation, and reactor design using Fe/AC catalyst

AE Mohammed, WT Mohammed, SA Gheni - Case Studies in Chemical and …, 2025 - Elsevier
Modeling and simulation at the bench scale are crucial for understanding industrial process
behavior, particularly in oxidative desulfurization (ODS). Mathematical models are powerful …

Integrated DNN and CFD model for real-time prediction of furnace waterwall slagging of coal-fired boiler

H Yin, X Liu, M Li, C Li, X Li, H Wang - Fuel, 2025 - Elsevier
Slagging of furnace waterwall adversely affects the thermal performance and increases the
operation and maintenance costs of coal-fired boilers. A real-time slagging prediction model …

Transformer neural networks with spatiotemporal attention for predictive control and optimization of industrial processes

ER Gallup, JF Tuttle, J Immonen… - 2024 American …, 2024 - ieeexplore.ieee.org
In the context of real-time optimization and model predictive control of industrial systems,
machine learning, and neural networks represent cutting-edge tools that hold promise for …