scholarly journals Retrieval of Optical Constant and Particle Size Distribution of Particulate Media Using the PSO-Based Neural Network Algorithm

10.5772/62446 ◽  
2016 ◽  
Author(s):  
Hong Qi ◽  
Ya-Tao Ren ◽  
Jun-You Zhang ◽  
Li-Ming Ruan ◽  
He-Ping Tan
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Optical Constant ◽  
Particulate Media ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Estimating Ore Particle Size Distribution using a Deep Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 12038-12043
Author(s):  
Laurentz E. Olivier ◽  
Michael G. Maritz ◽  
Ian K. Craig
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Convolutional Neural Network ◽  
Size Distribution ◽  
Deep Convolutional Neural Network

scholarly journals Neural network modelling to estimate particle size distribution based on other particle sections and meteorological parameters

2021 ◽  
Author(s):  
Pak Lun Fung ◽  
Martha Arbayani Zaidan ◽  
Ola Surakhi ◽  
Sasu Tarkoma ◽  
Tuukka Petäjä ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Meteorological Parameters ◽  
Aerosol Size Distribution ◽  
Sufficient Information ◽  
Electrical Mobility ◽  
Particle Sizer ◽  
Mass Concentrations

Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01 

scholarly journals Prediction of Aerosol Particle Size Distribution Based on Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yali Ren ◽  
Jiandong Mao ◽  
Hu Zhao ◽  
Chunyan Zhou ◽  
Xin Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Bp Neural Network ◽  
Scattering Theory ◽  
Mie Scattering ◽  
Aerosol Particle Size ◽  
Mie Scattering Theory

Aerosol plays a very important role in affecting the earth-atmosphere radiation budget, and particle size distribution is an important aerosol property parameter. Therefore, it is necessary to determine the particle size distribution. However, the particle size distribution determined by the particle extinction efficiency factor according to the Mie scattering theory is an ill-conditioned integral equation, namely, the Fredholm integral equation of the first kind, which is very difficult to solve. To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. For verifying the feasibility of the prediction model, some experiments were carried out. The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.

A neural network-based soft sensor for particle size distribution using image analysis

2011 ◽  
Vol 212 (2) ◽  
pp. 359-366 ◽  
Author(s):  
Young-Don Ko ◽  
Helen Shang
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Image Analysis ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Soft Sensor

The Optimization and Application of the Neural Network Together with Intelligence Methods

2011 ◽  
Vol 225-226 ◽  
pp. 581-584
Author(s):  
Shao Hua Xu ◽  
Hao Pan
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Coal Gangue ◽  
Intelligent Algorithm ◽  
The Neural Network ◽  
The Relationship

It needs a mass of data to correctly reflect a good many of complex factors and the performance of cement in the process of hydrating, researches of home and abroad are focus on data forecast by experiments and the Neural Network. The intelligent algorithm combined the Particles Algorithm and the Neural Network is used in this paper. The intelligent algorithm is applied in the process of the cement hydrating to forecast the relationship between the particle size distribution of coal gangue and expansible degree of cement.

scholarly journals A multilayer neural network for determination of particle size distribution in Dynamic Light Scattering problem

2019 ◽  
Vol 11 (2) ◽  
pp. 265-273 ◽  
Author(s):  
Ahmedhan Emilevich Shabanov ◽  
Mikhail Nikolaevich Petrov ◽  
Alexander Viktorovich Chikitkin
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Light Scattering ◽  
Dynamic Light Scattering ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Scattering Problem
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