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[HTML][HTML] A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks
Hydrogen production from different fuels has received extensive study interest owing to its
environmental sustainability, renewability, and lack of carbon emission. This research aims …
environmental sustainability, renewability, and lack of carbon emission. This research aims …
From characterization to discovery: artificial intelligence, machine learning and high-throughput experiments for heterogeneous catalyst design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Hybrid quantum neural network model with catalyst experimental validation: Application for the dry reforming of methane
Machine learning (ML), which has been increasingly applied to complex problems such as
catalyst development, encounters challenges in data collection and structuring. Quantum …
catalyst development, encounters challenges in data collection and structuring. Quantum …
Machine learning and density functional theory for catalyst and process design in hydrogen production
Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.
However, its production, especially green hydrogen generated from renewable sources, is …
However, its production, especially green hydrogen generated from renewable sources, is …
Carbon-efficient reaction optimization of nonoxidative direct methane conversion based on the integrated reactor system
Methane to olefins, aromatics, and hydrogen (MTOAH) has received considerable attention
because it can potentially provide an energy-efficient and environmentally friendly method …
because it can potentially provide an energy-efficient and environmentally friendly method …
Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation
Data consistency affects the robustness of machine learning-based models. Most
experimental and industrial data have low consistency, leading to poor generalization …
experimental and industrial data have low consistency, leading to poor generalization …
[HTML][HTML] Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study
In this study, the oligomerization of 1-octene was investigated using various zeolites through
both experimental and machine learning (ML) approaches. The structural characteristics of …
both experimental and machine learning (ML) approaches. The structural characteristics of …
[HTML][HTML] Machine learning-enhanced optimal catalyst selection for water-gas shift reaction
The water-gas shift (WGS) reaction is pivotal in industries aiming to convert carbon
monoxide, a byproduct of steam reforming of methane and other hydrocarbons, into carbon …
monoxide, a byproduct of steam reforming of methane and other hydrocarbons, into carbon …