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

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 

704 Measurements of Particle Size Distribution in Fuel Spray by Image Analysis

2001 ◽  
Vol 2001 (0) ◽  
pp. 237-238
Author(s):  
Masaya KIUCHI ◽  
Nobuyuki FUJISAWA ◽  
Akira HOSOKAWA ◽  
Shigeyuki TOMIMATSU
Keyword(s):  
Particle Size ◽  
Image Analysis ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Fuel Spray

Study of Particle Size Distribution of Selected Material of Solid Fertilizer by Using Photo-Optical Image Analysis

2014 ◽  
Vol 1059 ◽  
pp. 19-25
Author(s):  
Miroslav Macák ◽  
Ladislav Nozdrovický
Keyword(s):  
Particle Size ◽  
Image Analysis ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Production Systems ◽  
Physical And Mechanical Properties ◽  
Optical Image ◽  
National Standard ◽  
Sieve Analysis

In many branches of industrial production, there is a need for continual monitoring of the quality of manufactured product. Such requirements arise in the production of fertilizers, as the physical and mechanical properties of fertilizers affect the quality of application provided by fertilizer spreaders. The aim of the presented paper was to compare the suitability and applicability of the photo-optical image analysis with the sieve analysis used for the determination of fertilizer particle size distribution. The photo-optical method was used by [1] to study the fertilizer particle size distribution. These researchers tried to measure the size and velocity of flying particles in relation to the quality of application of centrifugal spreaders. During our comparative experiments, we have compared the photo-optical image analysis and sieve analysis. In experiments, we used the samples of the granulated fertilizer NMgS produced by the Duslo, a.s. company. The sieve analysis was conducted according to the national standard STN EN 1235 in the laboratories of the Department of Machines and Production Systems at the Faculty of Engineering, Slovak University of Agriculture in Nitra.

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