Neural network model-based predictive control of a distillation column—a neural network modelling methodology

1996 ◽  
Vol 18 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Paul Turner ◽  
Osvaldo Agammenoni ◽  
Geoff Barton ◽  
Julian Morris ◽  
Gary Montague
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Predictive Control ◽  
Neural Network Model ◽  
Distillation Column ◽  
Network Modelling ◽  
Model Based ◽  
Neural Network Modelling ◽  
Modelling Methodology

scholarly journals Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

2010 ◽  
Vol 17 (6) ◽  
pp. 809-815 ◽  
Author(s):  
A. Pasini ◽  
R. Langone ◽  
F. Maimone ◽  
V. Pelino
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Lorenz System ◽  
Lorenz Model ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Regime Transitions

Abstract. In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results.

A hybrid neural network model based modelling methodology for the rubber bushing

2021 ◽  
pp. 1-22
Author(s):  
Liangcheng Dai ◽  
Maoru Chi ◽  
Chuanbo Xu ◽  
Hongxing Gao ◽  
Jianfeng Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hybrid Neural Network ◽  
Model Based ◽  
Rubber Bushing ◽  
Modelling Methodology

scholarly journals Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2827 ◽  
Author(s):  
Daiki Ikeuchi ◽  
Alejandro Vargas-Uscategui ◽  
Xiaofeng Wu ◽  
Peter C. King
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Cold Spray ◽  
Network Model ◽  
Neural Network Model ◽  
Geometric Control ◽  
Geometric Feature ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
The Neural Network

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

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