Journal ArticleDOI
Performance prediction of pneumatic conveying of powders using artificial neural network method
J S Shijo,Niranjana Behera +1 more
TLDR
In this paper, the authors used ANN to predict the pneumatic conveying performance of powders in high and low air velocities, respectively, and used three different training methods: Levenberg Marquardt, Bayesian Regularization and Scaled Conjugate Gradient.About:
This article is published in Powder Technology.The article was published on 2021-08-01. It has received 7 citations till now. The article focuses on the topics: Pressure drop & Levenberg–Marquardt algorithm.read more
Citations
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Journal ArticleDOI
Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Journal ArticleDOI
Hybrid CFD-neural networks technique to predict circulating fluidized bed reactor riser hydrodynamics
TL;DR: In this article , an artificial neural network (ANN)-based machine learning model from a wide CFD simulation campaign was developed to predict axial solid holdup profile using a two-fluid model and validated using the experimental data.
Journal Article
Successful Implementation of Artificial Intelligence and Machine Learning in Multiphase Flow Smart Proxy Modeling: Two Case Studies of Gas-Liquid and Gas-Solid CFD Models
TL;DR: Two case studies of Smart Proxy Models (SPM) utilizing artificial intelligence (AI) and Machine Learning (ML) techniques to appraise the behavior of the chaotic system and predict the dynamic features including pressure, velocity and the evolution of phase fraction within the process at each time-step at a much lower run time are presented.
Journal ArticleDOI
Mass flow rate prediction of screw conveyor using artificial neural network method
TL;DR: In this paper , an Artificial Neural Network (ANN) was trained using DEM simulation results to reduce computational cost while keeping the estimation accuracy, and the results showed that the proposed model by ANN was also in a good agreement with the experimental data for horizontal screw conveyor.
Journal ArticleDOI
Experimental study and implementation of supervised machine learning algorithm to predict the flowability of two-phase water-oil in pipeline
TL;DR: In this article , the authors investigated the non-linear relationship between temperature and the flowability of two-phase water-oil in pipeline and showed that the sensitivity analysis revealed that the temperature has the most significant effect on the predicted output.
References
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Training feedforward networks with the Marquardt algorithm
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Neural network design
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Journal ArticleDOI
Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows
TL;DR: This work compares and analyzes the performance of artificial neural networks, ANN, and expert systems to flow pattern identification and extends to clustering algorithms to assist the formation of knowledge base employed during the learning stages of the ANNs and Expert systems.