Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Carbon dioxide (CO 2) is the major contributor to greenhouse gas (GHG) emissions and the
main driver of climate change. Currently, CO 2 utilization is increasingly attracting interest in …

Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

This is a survey of neural network applications in the real-world scenario. It provides a
taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of …

Artificial Neural Networks is a calculation method that builds several processing units based
on interconnected connections. The network consists of an arbitrary number of cells or …

This study presents a novel model for the simulation of co-gasification of rice husk and
plastic using Aspen Plus. The new approach involved using an artificial neural network …

Nanotechnology is a new technique which is widely applied in several energy systems. The
novel process of CO 2 absorption or conversion enhancement using nanofluids receives …