Use of neural networks in the analysis of particle size distribution by laser diffraction: tests with different particle systems

Guardani, Roberto; Nascimento, Cláudio Augusto Oller do; Onimaru, Rosalina Sumie

doi:10.1016/s0032-5910(02)00036-0

Cited by 28 publications

(21 citation statements)

References 11 publications

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“…Light scattering has emerged in recent decades as one of the more widely used techniques for particle size characterization due to advances in technology that improve the reproducibility of results, simplicity of use, detection range, and speed of analysis [46,47,48,49,50,51]. However, instrument resolution limitations have prevented full characterization of the submicron-sized fraction of powders until very recently [24,52]. It is now possible, using the latest generation of commercial light scattering instruments, to focus the detection and measurement on the submicron-sized fraction of powder.…”

Section: Visual Characterization Methodsmentioning

confidence: 99%

Method development and validation for measuring the particle size distribution of pentaerythritol tetranitrate (PETN) powders.

Young¹

2005

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Currently, the critical particle properties of pentaerythritol tetranitrate (PETN) that influence deflagration-to-detonation time in exploding bridge wire detonators (EBW) are not known in sufficient detail to allow development of a predictive failure model. The specific surface area (SSA) of many PETN powders has been measured using both permeametry and gas absorption methods and has been found to have a critical effect on EBW detonator performance. The permeametry measure of SSA is a function of particle shape, packed bed pore geometry, and particle size distribution (PSD). Yet there is a general lack of agreement in PSD measurements between laboratories, raising concerns regarding collaboration and complicating efforts to understand changes in EBW performance related to powder properties.i Benchmarking of data between laboratories that routinely perform detailed PSD characterization of powder samples and the determination of the most appropriate method to measure each PETN powder are necessary to discern correlations between performance and powder properties and to collaborate with partnering laboratories. To this end, a comparison was made of the PSD measured by three laboratories using their own standard procedures for light scattering instruments. Three PETN powder samples with different surface areas and particle morphologies were characterized. Differences in bulk PSD data generated by each laboratory were found to result from variations in sonication of the samples during preparation. The effect of this sonication was found to depend on particle morphology of the PETN samples, being deleterious to some PETN samples and advantageous for others in moderation. Discrepancies in the submicronsized particle characterization data were related to an instrument-specific artifact particular to one laboratory. The type of carrier fluid used by each laboratory to suspend the PETN particles for the light scattering measurement had no consistent effect on the resulting PSD data. Finally, the SSA of the three powders was measured using both permeametry and gas absorption methods, enabling the PSD to be linked to the SSA for these PETN powders. Consistent characterization of other PETN powders can be performed using the appropriate sample-specific preparation method, so that future studies can accurately identify the effect of changes in the PSD on the SSA and ultimately model EBW performance.ii iii ACKNOWLEDGMENTS

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Section: Visual Characterization Methodsmentioning

confidence: 99%

Method development and validation for measuring the particle size distribution of pentaerythritol tetranitrate (PETN) powders.

Young¹

2005

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“…Other methods were also developed to retrieve the particle size distribution, including least squares optimization algorithm (Qiao and Zhang 2004;Su et al 2005), Powell optimization algorithm (Liu and Zheng 1998), neural network algorithm (Guardani et al 2002), dynamic system identification (Zheng et al 2002), and simulated annealing algorithm. These methods have been widely used in the measurement of air particles and liquid suspensions.…”

Section: Introductionmentioning

confidence: 99%

Dust Particle Size Distribution Inversion Based on the Multi Population Genetic Algorithm

Mao¹,

Li²

2014

Terr. Atmos. Ocean. Sci.

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The aerosol number size distribution is the main parameter for characterizing aerosol optical properties and physical properties, it has a major influence on radiation forcing. With regard to some disadvantages in the traditional methods, a method based on the multi population genetic algorithm (MPGA) is proposed and employed to retrieve the aerosol size distribution of dust particles. The MPGA principles and design are presented in detail. The MPGA has better performance compared with conventional methods. In order to verify the feasibility of the inversion method, the measured aerosol optical thickness (AOT) data of dust particles taken by a sun photometer are used and a series of comparisons between the simple genetic algorithm (SGA) and MPGA are carried out. The results show that the MPGA presents better properties when compared with the SGA with smaller inversion errors, smaller population size and fewer generation numbers to retrieve the aerosol size distribution. The MPGA inversion method is analyzed using the background day, dust storm event and seasonal size distribution. The method proposed in this study has important applications and reference value for aerosol particle size distribution inversion. Atmos. Ocean. Sci., 25,[791][792][793][794][795][796][797][798][799][800]

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“…Although forward laser diffraction methods have become increasingly popular due to reproducibility and easiness of operation, they cannot be used in measuring CSD in highly concentrated suspensions, unless the optical model is replaced with empirically fitted models based on neural networks [2,3], thus enabling the on-line monitoring of crystallization processes under certain conditions [4].…”

Section: Introductionmentioning

confidence: 99%

In‐Line Monitoring of Crystallization Processes Using a Laser Reflection Sensor and a Neural Network Model

Giulietti¹,

Guardani²,

Nascimento³

et al. 2003

Chem Eng & Technol

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Laboratory-scale experiments were carried out for measuring the chord length distribution of different particle systems using a laser reflection sensor. Samples consisted of monodisperse, polydisperse and bimodal FCC catalyst and PVC particles of different sizes, ranging from about 20 to 500 lm. The particles were dispersed in water, forming suspensions with solid-phase mass fractions ranging from ca. 0.2 % until ca. 30 %. The experimental results, consisting of the particle number counting per chord length class, were used in fitting a neural network model for estimating the mass concentration of particles in the suspension and the volume-based size distribution, eliminating the effects of suspension concentration and particle shape. The results indicate the feasibility of using such a model as a software sensor in crystallization processes monitoring.

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Use of neural networks in the analysis of particle size distribution by laser diffraction: tests with different particle systems

Cited by 28 publications

References 11 publications

Method development and validation for measuring the particle size distribution of pentaerythritol tetranitrate (PETN) powders.

Method development and validation for measuring the particle size distribution of pentaerythritol tetranitrate (PETN) powders.

Dust Particle Size Distribution Inversion Based on the Multi Population Genetic Algorithm

In‐Line Monitoring of Crystallization Processes Using a Laser Reflection Sensor and a Neural Network Model

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