Results 131 to 140 of about 129,225 (287)

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

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

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

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

A direct normal irradiation forecasting model based on artificial neural networks

Revue des Énergies Renouvelables, 2016
We investigate the forecasting of the hourly Direct Normal Irradiation (DNI) using Artificial Neural Networks (ANN). The data used are hourly satellite data for the region of Ouarzazate in the South West Mediterranean basin region.
I. Belhaj, O. El Fatni, S. Barhmi, E.H. Saidi   +3 more
doaj  

Depressive symptoms as independent correlates of epilepsy‐related cognitive burden

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to assess the relationship between the severity of depression and anxiety symptoms and epilepsy‐related variables and cognitive burden in people with epilepsy (PwE), as assessed using EpiTrack. Methods We prospectively enrolled a cohort of PwE who underwent EpiTrack and evaluation by Generalized Anxiety ...
Biagio Maria Sancetta, Giuli Lippa, Marianna Nesta, Lorenzo Ricci, Simona Paola Carbone, Lorenzo Veronese, Giulia Conti, Marco Sferruzzi, Vincenzo Di Lazzaro, Mario Tombini, Giovanni Assenza   +10 more
wiley   +1 more source

An algorithm for seizure detection in rodents

Epilepsia Open, EarlyView.
Abstract Objective Epilepsy animal research often relies on long‐term intracranial electroencephalographic (iEEG) recordings. Here, we describe an artificial neural network (ANN) algorithm for automatic detection of seizures. Methods The algorithm was trained on iEEG recordings of three mouse models of chronic epilepsy: (1) the pilocarpine model of ...
Lyna Kamintsky, Gerben van Hameren, Itai Weissberg, Pooyan Moradi, Ofer Prager, Alaa Abu Ahmad, Lior Schori, Albert Becker, Yaniv Zigel, Alon Friedman   +9 more
wiley   +1 more source

Research on Deformation Prediction of Small Interval Tunnel Based on Machine Learning and Numerical Simulation

Energy Science &Engineering, EarlyView.
This study leverages machine learning algorithms—specifically artificial neural networks (ANN) and genetic programming (GP)—to forecast and analyze variations in vault settlement measurements during excavation of small interval tunnel. A settlement prediction model was developed and validated through comparative analysis with regression to evaluate the
Wenjie Zhai, Jia Xue, Yongjie Fu, Beichen Yu, Yan Zhu, Man Yuan, Mingbo Chi, Zhentao Li   +7 more
wiley   +1 more source

Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm

Energy Science &Engineering, EarlyView.
Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson, Sivarani Thankamony Saradha Devi, Kumar Chandrasekaran, Sourav Barua   +3 more
wiley   +1 more source

DETERMINATION OF THE NEXT STOPPING FLOOR IN ELEVATOR TRAFFIC CONTROL BY MEANS OF NEURAL NETWORKS

Electrica, 2005
When a group of lifts serve together it is important coordinate the movements of the individual lifts in such a way that the lift group should operate efficiently.
C.Erdem İMRAK, Mustafa ÖZKIRIM
doaj