Results 211 to 220 of about 49,842 (269)

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source

Box–Behnken optimization and ANN‐ANFIS integrated modelling for transesterification using thermally activated basic oxygen furnace slag catalyst

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Global energy demand and environmental concerns have intensified the search for renewable and sustainable energy sources. This study thus, focuses on optimizing the transesterification process of waste cooking oil (WCO) using thermally activated basic oxygen furnace slag catalyst calcined at 850°C (BOF 850). The optimization and modelling were
Johra S. Ali, Hillary L. Rutto
wiley   +1 more source

Application of artificial neural network and general machine learning modelling on CO2 adsorption in moisture equilibrated South African high and medium rank coals

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall, Lawrence Koech, Douw Faurie, Khanyisile Lepota   +3 more
wiley   +1 more source

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

The Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin, Clémence Fauteux‐Lefebvre, Jules Thibault   +2 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Artificial Neural Networks (ANN)

2007
This chapter introduces the basic principles of artificial neural networks (ANN) as computational models that mimic the brain in its main principles. Theyhavebeenused so far to model brain functions, along with solving complex problems of classification, prediction, etc. in all areas of science, engineering, technology and business.
Lubica Benuskova, Nikola Kasabov
openaire   +1 more source

Application of Artificial Neural Networks (ANNs) in Wine Technology

Critical Reviews in Food Science and Nutrition, 2013
In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
Halil, Baykal, Hatice Kalkan, Yildirim
openaire   +2 more sources

Artificial neural networks (ANNs) and modeling of powder flow

International Journal of Pharmaceutics, 2003
Effects of micromeritic properties (bulk, tapped and particle density, particle size and shape) on the flow rate through circular orifices are investigated, for three pharmaceutical excipients (Lactose, Emcompress and Starch) separated in four sieve fractions, and are modeled with the help of artificial neural networks (ANNs).
K, Kachrimanis, V, Karamyan, S, Malamataris   +2 more
openaire   +2 more sources

Applications of Artificial Neural Networks (ANNs) in Food Science

Critical Reviews in Food Science and Nutrition, 2007
Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and ...
Yiqun, Huang, Lars J, Kangas, Barbara A, Rasco   +2 more
openaire   +2 more sources

Instability localization with artificial neural networks (ANNs)

Annals of Nuclear Energy, 2002
Abstract The aim of this piece of research is to investigate the potential of artificial neural networks (ANNs) for tackling the problem of instability localization. The instability is modeled by a variable strength absorber (point-source) in a two-dimensional bare reactor model with a one neutron-energy group.
T. Tambouratzis, M. Antonopoulos-Domis
openaire   +1 more source