[HTML][HTML] Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants
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 …
nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these …
[HTML][HTML] Digital innovation's contribution to sustainability transitions
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 …
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 …
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
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 …
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
This work involves the Industry 4.0 infrastructure developed at West Virginia University
(WVU) for process systems applications. This infrastructure emulates an interconnected …
(WVU) for process systems applications. This infrastructure emulates an interconnected …
Physics-guided neural networks with engineering domain knowledge for hybrid process modeling
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 …
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
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 …
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 …
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 …
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
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 …
machine learning, and neural networks represent cutting-edge tools that hold promise for …