Application of Intelligent Models in Advanced Clean Fuel Energy Systems

The demand for energy has been growing all over the world owing to industrial advancements and population growth. Recently, many scientists have focused on replacing fossil fuels with renewable energies, while others have made efforts to optimize the processes in the non-renewable industries. One of the cutting-edge technologies used in the area of non-renewable energy systems to ensure they can be made more clean is artificial intelligence (AI). The power of AI has already been shown to revolutionise many fields of study, with its approaches being used for utilized regression, optimization, and classification purposes. Furthermore, AI has enabled the introduction of increasing efficiency through the implementation of features such as saving the time of calculations, considerable adaptivity, uniform analysis capability, the high ability for noise control, and great computational rate can be considered as the reason for their never-ending reputation and popularity. The use of these approaches can significantly aid the non-renewable energy industries to become more efficient, clean and green as the world shifts from older fuels in pursuit of a more sustainable future.

This Research Topic is devoted to the latest advancements in intelligent models applied to address challenges and obstacles in non-renewable energy systems. Scientists from around the world are welcome to contribute to developing a comprehensive collection of papers on the progressive and high-impact realm of intelligent models. Papers with strong algorithmic and modeling innovations, as well as case studies and state-of-the-art reviews are particularly welcomed.

For this Research Topic, we are seeking contributions that demonstrate the use of AI in making more traditional non-renewable energy sources more clean and sustainable. Potential contributors are encouraged to submit research on intelligent models to empower non-renewable energy industries in a broad range of applications, including, but not limited to, the following:

• Enhanced oil recovery (EOR)

• Flow assurance

• Asphaltene and wax precipitation

• Interfacial phenomenon

• Unconventional reservoirs

• Energy Conversion and management

• Sustainable energy

• Energy saving

• Energy price prediction

• Energy and climate change

• Smart urban energy systems

• Machine learning

• Deep learning

• Hybrid and ensemble models

• Soft computing

• Minerals

• Clean coal technologies