Quantification of particle size and concentration using in-line techniques and multivariate analysis (original) (raw)
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Chemical Engineering Science, 2016
Efficient processing of particulate products across various manufacturing steps requires that particles possess desired attributes such as size and shape. Controlling the particle production process to obtain required attributes will be greatly facilitated using robust algorithms providing the size and shape information of the particles from in situ measurements. However, obtaining particle size and shape information in situ during manufacturing has been a big challenge. This is because the problem of estimating particle size and shape (aspect ratio) from signals provided by in-line measuring tools is often ill posed, and therefore it calls for appropriate constraints to be imposed on the problem. One way to constrain uncertainty in estimation of particle size and shape from in-line measurements is to combine data from different measurements such as chord length distribution (CLD) and imaging. This paper presents two different methods for combining imaging and CLD data obtained with in-line tools in order to get reliable estimates of particle size distribution and aspect ratio, where the imaging data is used to constrain the search space for an aspect ratio from the CLD data.
Challenges in particle size distribution measurement past, present and for the 21st century
Progress in Organic Coatings, 1997
The field of particle size distribution (PSD) characterization and measurement has experienced a renaissance over the past ten years. This revitalization has been driven by advances in electronics, computer technology and sensor technology in conjunction with the market pull for PSD methods embodied in cost effective user friendly instrumentation. The renaissance can be characterized by at least four activities.
Comparison of Particle Size Distributions Measured Using Different Techniques
Particulate Science and Technology, 2005
In this article, particle size distributions (PSDs) measured by different techniques, including image analysis (IA), laser diffraction (LD), ultrasonic attenuation spectroscopy (UAS), and focused-beam reflectance measurement (FBRM), are compared for spherical glass beads and nonspherical silica flakes. It is shown that particle shape strongly affects the results obtained by different techniques. For spheres, the PSDs obtained by IA, LD, and UAS agree well. There is no consistent result among different particle measurement techniques for nonspherical particles. The conversion between PSDs obtained by IA, LD, and UAS has been based on particle shape factors. Caution must be exercised when a measured chord length distribution (CLD) is used to indicate the PSD during a process because the CLD result obtained by FBRM is complex, depending not only on the PSD, but also on particle optical properties and shape. Keywords Particle size distribution (PSD), image analysis (IA), laser diffraction (LD), ultrasonic attenuation spectroscopy (UAS), focused-beam reflectance measurement (FBRM), chord length distribution (CLD)
AAPS PharmSciTech, 2006
The purpose of this paper is to describe results from the use of a set of Excel macros written to facilitate the comparison of image analysis (IA) and laser diffraction (LD) particle size analysis (psa) data. Measurements were made on particle systems of differing morphological characteristics including differing average aspect ratios, particle size distribution widths and modalities. The IA and LD psa data were plotted on the same graph treating both the weighting and the size unit of the LD psa data as unknowns. Congruency of the IA and LD plots was considered to indicate successful experimental determination of the weighting and size unit. The weighting of the resulting LD psa data (so-called volume-weighted) is shown to be better correlated with IA area-weighted data. The size unit of LD psa data is shown to be a function of particle shape. In the case of high aspect ratio particles characterized by approximately rectangular faces the LD psa data is shown to be a function of multiple particle dimensions being related to IA size descriptors through a simple variation of the law of mixtures. The results demonstrate that successful correlations between IA and LD psa data can be realized in the case of non-spherical particle systems even in the case of high aspect ratio particles; however, the inappropriateness of the application of the Equivalent Spherical Volume Diameter and the Random Particle Orientation assumptions to the interpretation of the LD psa results must first be acknowledged. Correlation permits cross validation of IA and LD psa results increasing confidence in the accuracy of the data from each orthogonal technique.
arXiv (Cornell University), 2018
The in situ measurement of the particle size distribution (PSD) of a suspension of particles presents huge challenges. Various effects from the process could introduce noise to the data from which the PSD is estimated. This in turn could lead to the occurrence of artificial peaks in the estimated PSD. Limitations in the models used in the PSD estimation could also lead to the occurrence of these artificial peaks. This could pose a significant challenge to in situ monitoring of particulate processes, as there will be no independent estimate of the PSD to allow a discrimination of the artificial peaks to be carried out.
Novel probe for the in situ measurement of particle size distributions
Review of Scientific Instruments, 2006
The development of a novel instrument for the in situ measurement of particle size distributions in the size range of 3–200μm is presented. The system uses high magnification optics, housed in a stainless steel probe, which can be inserted into a process stream or vessel, where images of the dispersed phase particles are recorded. A pulsed light source is used to freeze the motion of the particles in the field of view and present an image of the dispersion onto a charge-coupled device camera chip. The images are digitized and stored for later processing. Automated image analysis routines have been developed for extracting particle size information from the acquired images. An extensive validation of the instrument has been performed for spherical particles, which has produced several important findings. First, a size bias in the depth of field (DOF) exists which favors larger particles. An experiment procedure was developed for the direct measurement of DOF size biases. Additionally...
Particle size distribution quantification by microscopic observation
Journal of Aerosol Science, 2004
The particle size distributions measured by the optical microscope (OPM) were compared with those by the light scattering particle counter (PC) to validate the microscopic method for particle size distribution quantiÿcation. While the OPM concentrations increased with the PC concentrations, the OPM concentrations tended to be higher than those by the PC. To explain the di erence between the two methods, we estimated their relationship based on the Mie scattering theory. The calculation roughly estimated the particle geometric diameters in theory were 1.7-2.0 times as large as the corresponding PC readouts. Using these theoretical factors, the size ranges of the PC were converted to match with the OPM measurements (PC * ). Overall, the OPM concentrations were lower than the PC * concentrations. The advantage in the OPM method particularly for ambient aerosols lied in its accuracy of particle sizing although the counting e ciency might be lowered due to its intrinsic limitations such as inability of counting particles having the similar refractive index of the ÿlter. ?
Powder Technology, 2013
This paper highlights the fact that particle size distribution (PSD) is not unique for the same product, and is dependent on the chosen measurement technique, especially for asymmetric shapes. Laser diffraction and 2D image analysis are commonly used PSD measurement techniques. However, the results may not be representative of the true physical dimensions of the particles. The influence of particle shape on PSD results obtained from 2D/3D image analysis and laser diffraction was investigated. Two metallic powders presenting extreme shape properties (round and elongated particles) were analyzed, as well as a blend of the two pure products. 2D image analysis and laser diffraction results were compared to 3D image analysis (measuring the true particle size). This paper compares the PSD results obtained from the three methods. Some commonly used size parameters in image analysis software did not give meaningful results in regard of the true physical dimensions of the particles. The existence of the two populations (products with extremely different shape and size characteristics) could not be identified with such size parameters, and laser diffraction also performed poorly. The PSD obtained from more precise size parameters (image analysis) better corresponded to the true dimensions of the particles. This study highlights the strengths and weaknesses of particle size analysis techniques when studying products presenting diverse particle shapes, and points out that caution is required in the choice of the size parameters, and in the interpretation of PSD results.
Obtaining Particle Size Distribution from Chord Length Measurements
Particle & Particle Systems Characterization, 2006
The Lasentec focused-beam reflectance measurement (FBRM) is becoming a more popular technique to measure particle size on-line in different applications. The FBRM uses a focused beam of laser light that scans across particles passing in front of the probe window to measure a chord length distribution (CLD). Compared with CLD information, the particle size distribution (PSD) is more useful because it is directly related to product quality and process productivity. However, it is not straightforward to convert a measured CLD into its corresponding PSD accurately due to the lack of a theoretical analysis for non-spherical particle systems. In this paper, firstly a general model to translate a PSD into its corresponding CLD is given for different shapes including spherical, ellipsoidal and more general non-spherical particles. Then an iterative inversion method is developed to obtain the PSD from a measured CLD. Finally, effectiveness of the proposed PSD-CLD model and iterative inversion method has been extensively validated by experiments.
A simple and fast matlab-based particle size distribution analysis tool
International Journal of Computational Methods and Experimental Measurements, 2021
Particle size distribution is one of the most important physical properties of a particulate sample. Traditional particle-sizing methods to estimate a geometrical particle size distribution employ a sieve analysis (or gradation test), which entails filtering the particles through a series of sieves and measuring the weight remaining on each sieve to estimate the number-weighted particle size distribution. However, these two quantities have the same value only if particles are perfectly spherical and round. On the other hand, a particle sizer such as the Malvern particle size analyzer, which uses laser diagnostics to measure the particle sizes, can be a hefty investment. Alternatively, imaging techniques can be applied to estimate the size of these particles by scaling a reference dimension to the pixel size, which in turn is used to estimate the size of the visible particles. The focus of this work is to present a simple methodology using a DSLR camera and an illuminated LED panel to generate enough contrast. Using the camera and lens properties, the scale, or size, of any image can be obtained based on the mounting distance of the camera with respect to the target. An analysis tool was developed in MATLAB where the images are processed automatically based on the prescribed camera and lens properties embedded within the same image file and requiring the user to only input the mounting distance of the camera. So far, results show a positive agreement when comparing to measurements using ImageJ imaging tools and a sieve analysis. Future tests will analyze different particle sizes and types, as well as using a Malvern particle size analyzer to corroborate the results.