Propylene Polymerization Reactor Control and Estimation Using a Particle Filter and Neural Network

2017 ◽  
Vol 11 (6) ◽  
pp. 1700010 ◽  
Author(s):  
Ana Carolina Spindola Rangel Dias ◽  
Wellington Betencurte da Silva ◽  
Julio Cesar Sampaio Dutra
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Propylene Polymerization ◽  
Reactor Control ◽  
Polymerization Reactor

A stacked neural network approach for yield prediction of propylene polymerization

2009 ◽  
pp. NA-NA
Author(s):  
Seyed Ali Monemian ◽  
Hamed Shahsavan ◽  
Oberon Bolouri ◽  
Shahrouz Taranejoo ◽  
Vahabodin Goodarzi ◽  
...  
Keyword(s):  
Neural Network ◽  
Propylene Polymerization ◽  
Yield Prediction ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3350 ◽  
Author(s):  
Kittipong Kasantikul ◽  
Dongkai Yang ◽  
Qiang Wang ◽  
Aung Lwin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Particle Filter ◽  
Maritime Transportation ◽  
Global Navigation Satellite Systems ◽  
Speed Estimation ◽  
Estimation Model ◽  
Main Concept ◽  
Artificial Neural

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

scholarly journals Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure

Energy and AI ◽  
2020 ◽  
Vol 2 ◽  
pp. 100017 ◽  
Author(s):  
Renyou Xie ◽  
Rui Ma ◽  
Sicheng Pu ◽  
Liangcai Xu ◽  
Dongdong Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Particle Filter ◽  
Recurrent Neural Network

scholarly journals Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882157
Author(s):  
Pengyun Chen ◽  
Jianlong Chang ◽  
Yujie Han ◽  
Meini Yuan
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Particle Filter ◽  
Basis Function ◽  
Particle Filtering ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Gaussian Sum ◽  
Matching Performance ◽  
Navigation Method

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.

Constrained optimal batch polymerization reactor control

1990 ◽  
Vol 30 (19) ◽  
pp. 1209-1219 ◽  
Author(s):  
Argimiro R. Secchi ◽  
Enrique Luis Lima ◽  
José Carlos Pinto
Keyword(s):  
Reactor Control ◽  
Optimal Batch ◽  
Polymerization Reactor
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