Method For Particle Size Distribution Research Articles

A Fundamental Study on the Extraction of Particulate Organic Carbon from Municipal Wastewater Treatment Plants

In line with the strategy of transforming existing municipal wastewater treatment plants (WWTP) from disposal facilities into systems for using domestic wastewater (WW) as a source of energy and raw materials, a concept consisting of chemical, physical, and biological steps has been set up as a pilot project in WWTP Büsnau, Stuttgart, Germany. The key part of the entire process is based on the use of advanced microsieving (MS) to eliminate remaining particulate organic carbon (POC) (mg/L) from the effluent of the primary sedimentation tank (PST). Therefore, in the primary stages of this project, it was necessary to have a broad vision and a true understanding of the particle size distribution (PSD) of municipal WW. As a novel approach, in the present study, the conventional PSD method was optimized by implementing certain modifications, and the tests were conducted in situ. The modified PSD analyses facilitated in-depth investigations of solid–liquid separation at WWTPs and showed that drying samples in the oven can result in a 20% to 30% deviation in the POC (mg/L) removal results. In addition, the idea of the substitution of PSTs with an MS was supported by the results of this study. It was determined that an MS with a pore size of 45 µm to 63 µm can provide the same elimination efficiency as a PST. Another significant outcome of this study was the introduction of suitable mesh sizes for the MS which were coupled with PST in order to extract the maximum amount of POC (mg/L) from the municipal WWTPs without the addition of any chemicals. The results revealed that up to 90% of the TSS (mg/L) and 70% of the COD (mg/L) can be removed if an MS with a mesh size between 4 µm and 20 µm is coupled with a PST.

Predictive control of particlesize distribution of crystallization process using deep learning based image analysis

AbstractThe challenges to regulate the particle‐size distribution (PSD) stem from on‐line measurement of the full distribution and the distributed nature of crystallization process. In this article, a novel nonlinear model predictive control method of PSD for crystallization process is proposed. Radial basis function neural network is adopted to approximate the PSD such that the population balance model with distributed nature can be transformed into the ordinary differential equation (ODE) models. Data driven nonlinear prediction model of the crystallization process is then constructed from the input and output data and further be used in the proposed nonlinear model predictive control algorithm. A deep learning based image analysis technology is developed for online measurement of the PSD. The proposed PSD control method is experimentally implemented on a jacketed batch crystallizer. The results of crystallization experiments demonstrate the effectiveness of the proposed control method.

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.

Comparison of laser diffraction and image analysis techniques for particle size-shape characterization in additive manufacturing applications

Powder-based additive manufacturing (AM) technologies are capable of producing high-performance parts for a diverse set of applications. Many AM processes require specific tolerances on feedstock powder to be successful, necessitating high-quality, precise measurements of particle size distribution (PSD) and morphology. This study investigates the use of several powder PSD and morphology measurement methods—including laser diffraction (LD)-based and static image analysis (SIA) techniques—for AM applications. Four AM powders of varying size and shape were tested to compare the influence of data collection methodology on reported powder characteristics. Results indicate that a disparity exists between the PSD and powder morphology data collected using the three method types evaluated, particularly when measuring fine and irregular powder particles. Considering the advantages and disadvantages of both SIA and LD-based methods, consistent use of methodological approach is recommended when analyzing powder PSD and morphology for AM applications.

A General Review on Methods of Sediment Sampling and Mineral Content Analysis

Sediment erosion is a major problem in hydropower plants of Himalayan and Andes region which causes frequent breakdown of electromechanical parts in contact with sediment laden water demanding regular maintenance. Sediment suspended in water with high velocity has higher potential to erosion of the turbine parts. Studies show that sediment erosion can be minimized to some extent if modifications in the design, use of erosion resistant material as well as application of erosion resistant paints are carried out on those components. Besides, thorough understanding of sediment by proper analysis is important for designing and making components less prone to sediment erosion. The efficiency of sediment analysis depends on the sampling method and the method used for particle size distribution (PSD) analysis and mineral content (MC) analysis. Depending on the nature of the stream, a different method should be adopted. This paper presents a general overview of the method of sampling, method of PSD and MC analysis.

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For the first time in oil sands industry, the absolute amounts of <2 μm clay and <0.2 μm ultrafine solids were quantified in commercial streams from the naphthenic froth treatment (NFT) process used by mined oil sands operators. The sample suite studied comprised seven representative NFT streams collected at a commercial plant including: one bitumen froth feed (F1), two naphtha-diluted bitumen products (P1, P2) and four tailings (T1, T2, T3, T4). The solids present in each of the seven NFT streams were fractionated quantitatively based on particle size and solids wettability using three original quantitative separation techniques presented here. First, clay-ultrafines (CUF) separation technique allows to fractionate solids into four distinct particle-size fractions: coarser solids (CS), >2 μm; total clays (TC), <2 μm; coarse clays (CC), 0.2–2 μm; and ultrafines (UF), <0.2 μm. Second, toluene/water (T/W) interfacial wettability (WET) fraction separation technique enables separation of solids into four distinct fractions: Hydrophilic Solids (HPS) partitioning into the aqueous phase (carbon <5% w/w); Intermediate Solids (IS) only weakly held at the T/W interface (carbon 6–10% w/w); organic-rich solids (ORS) remaining strongly held at the T/W interface (carbon 15–20% w/w); high speed centrifugation solids (HSCS) remaining dispersed in the bitumen-toluene phase (carbon >20% w/w). And third, toluene/water interfacial total solids (TS) separation permits to isolate all-at-once the entire solids fraction comprised in a given sample. The analytical procedures developed to apply these three techniques are outlined in detail, and compared to two standard methods used in the oil sands industry: Soxhlet-Dean and Stark extraction method and COSIA Unified Fines Method for minus 44 µm material and for Particle Size Distribution (PSD). From quantitative results obtained by CUF, WET and TS techniques and industry standard PSD methods, the following key results arise. Most notably, CUF technique developed here showed >10 wt% ultrafines in samples T4, P1 and P2, whereas industry standard PSD methods (laser diffraction PSD data) did not notice any significant differences between the seven samples. The UF contents per total solids increase in going from froth F1 (∼2.5 wt%) through product P1 (∼9.3 wt%) to product P2 (∼11 wt%). Tailings T4 has the highest proportion of UF (∼12 wt%) amongst the four tailings, and also has the highest amounts of HPS, TC, and CC of any streams. NFT stream T4 therefore likely results from a separation process which selectively removes the smaller hydrophilic solids. The ORS and HSCS proportions per total solids increase in going from froth F1 through product P1 to product P2. For product P2, the ORS and HSCS contents account respectively for ∼70 wt% and ∼10 wt% of the solids contained in this sample. By contrast, as ORS and HSCS increase, the HPS contents decrease correspondingly. This inverse behavior between HPS and ORS + HSCS contents demonstrates the preferential removal of hydrophilic solids by the NFT processes. X-ray diffraction patterns measured for HSCS fractions separated from froth F1 and products P1 and P2 are virtually the same. This suggests that HSCS solids are present in the inlet froth and go through the entire NFT circuit without being affected by any of the different separation unit operations integrated in the NFT process flowsheet.

Structural and electrochemical study of fast Li diffusion in Li3V2(PO4)3-based electrode material

NASICON-type lithium-vanadium phosphate (Li3V2(PO4)3) based electrodes capable to provide extremely fast lithium transport properties were studied by a combination of structure, morphology and surface characterization methods: X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), laser diffraction particle size distribution (PSD), as well as electrochemical methods: potentiostatic (PITT) and galvanostatic (GITT) intermittent titration techniques, electrochemical impedance spectroscopy (EIS), and constant current chronopotentiometry. Significant differences in the kinetics of reversible lithium intercalation depending on the lithiation stage were found: lithium diffusion coefficients, found from the PITT, GITT and EIS data, demonstrate an abrupt drop by 2–3 orders of magnitude (from 10−9 to 10−12cm2s−1) in the 4.3–4.4V potential range vs Li/Li+, which is attributed to the LiV2(PO4)3↔V2(PO4)3 phase transition. The electrochemical extraction/insertion of two lithium equivalents can occur at ultra-high rates (up to 320C) from/into structurally more accessible Li2 and Li3 sites, while the de/intercalation of the third lithium equivalent from/into the Li1 position is supposedly hindered kinetically. To analyze the electrochemical data, specially developed theoretical models were used, which take into account geometry and phase configuration of the diffusion space, as well as the properties of the phase boundary interfaces. Morphology and size distribution parameters, essential for mathematical processing of the electrochemical data, were obtained by SEM, PSD and BET methods.

Particle size distribution modeling of milled coals by dynamic image analysis and mechanical sieving

ABSTRACT Particle size distribution (PSD) of lignite and hard coals ground by ball and Gy-Ro mills were determined and compared using mechanical sieving (MS; direct, width-classification) and dynamic image analysis (DIA; indirect, length-classification), expressed in terms of sieves opening diameter and equivalent circular area diameter. Ground coal PSD data (eight combinations) were visualized and studied using length-transformation on MS and DIA using log-normal distribution plots. Data were also analyzed and compared by developing PSD models, namely Gaudin–Schuhmann (GS) and Rosin–Rammler (RR). In general, in the fine particle range below 100 μm, all ground coal samples exhibited similar PSD, for both MS and DIA methods. On PSD modeling, RR model ( R 2 ≥ 0.81; AIC ≤ − 8.6) fitted well the observation over the entire range of particles sizes produced by both ball and Gy-Ro mills compared to GS model ( R 2 ≥ 0.69; AIC ≤ − 7.3). DIA can easily determine the particle size ranges below 100 μm, while MS have only limited sieves in this range. Besides, DIA has produced more accurate PSD results than MS especially 38 μm and below. Thus, DIA and RR model can be recommended for PSD analysis of fine particulate coals, minerals, and similar products.

Correlation of Drug Substance Particle Size Distribution with Other Bulk Properties to Predict Critical Quality Attributes

The purpose of this study was to correlate the particle size distribution (PSD) of a development molecule (1) with other bulk properties such as surface area and powder flow, to enable prediction of critical quality attributes (CQA) of the drug substance to support a roller compaction formulation process. Dry dispersion laser diffraction, wet dispersion laser diffraction, and wet dispersion image analysis PSD methods were developed for 1. Twenty eight (28) process batches were characterized by all three methods, and the resulting data were correlated to other bulk properties to determine if these correlations could be used to predict CQA values for future process batches. PSD metrics measured with wet dispersion laser diffraction and wet dispersion image analysis methods were found to have significantly better correlation to other bulk properties when compared to the metrics measured using the dry dispersion laser diffraction method. The correlations generated were found to be useful to predict resulting surface area and flow function values during process development. On the basis of these correlations, a target PSD range was identified for 1 that could support the roller compaction process. In order to further validate this methodology, various grades of lactose monohydrate were then used as a second model system to test correlation between PSD and other bulk properties.

Hot-stage microscopy for determination of API particles in a formulated tablet.

Although methods exist to readily determine the particle size distribution (PSD) of an active pharmaceutical ingredient (API) before its formulation into a final product, the primary challenge is to develop a method to determine the PSD of APIs in a finished tablet. To address the limitations of existing PSD methods, we used hot-stage microscopy to observe tablet disintegration during temperature change and, thus, reveal the API particles in a tablet. Both mechanical and liquid disintegration were evaluated after we had identified optimum milling time for mechanical disintegration and optimum volume of water for liquid disintegration. In each case, hot-stage micrographs, taken before and after the API melting point, were compared with image analysis software to obtain the PSDs. Then, the PSDs of the APIs from the disintegrated tablets were compared with the PSDs of raw APIs. Good agreement was obtained, thereby confirming the robustness of our methodology. The availability of such a method equips pharmaceutical scientists with an in vitro assessment method that will more reliably determine the PSD of active substances in finished tablets.

Particle size analysis of metal powders using laser diffraction : M.R.Bumiller. (Malvern Instruments Inc, USA.)

Powder-based additive manufacturing (AM) technologies are capable of producing high-performance parts for a diverse set of applications. Many AM processes require specific tolerances on feedstock powder to be successful, necessitating high-quality, precise measurements of particle size distribution (PSD) and morphology. This study investigates the use of several powder PSD and morphology measurement methods—including laser diffraction (LD)-based and static image analysis (SIA) techniques—for AM applications. Four AM powders of varying size and shape were tested to compare the influence of data collection methodology on reported powder characteristics. Results indicate that a disparity exists between the PSD and powder morphology data collected using the three method types evaluated, particularly when measuring fine and irregular powder particles. Considering the advantages and disadvantages of both SIA and LD-based methods, consistent use of methodological approach is recommended when analyzing powder PSD and morphology for AM applications.

Challenges in particle size distribution measurement past, present and for the 21st century

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. (1) End user innovation exemplified by techniques such as hydrodynamic chromatography (HDC), capillary hydrodynamic fractionation (CHDF) and field flow fractionation methods (SdFFF, FlFFF, and ThFFF). (2) Revitalization of older instrumental methods such as gravitational and centrifugal sedimentation; (3) Evolution of research grade instrumentation into low cost, routine, user friendly instrumentation exemplified by dynamic light scattering (DLS). (4) The attempt to meet extremely difficult technical challenges such as: (a) providing a single hybrid instrument with high resolution over a very broad dynamic range (4+ decades in size; e.g., Fraunhofer/Mie; photozone sensing/DLS); (b) PSD measurement of concentrated dispersions (acoustophoretic, dielectric measurements, fiber optic DLS (FOQELS)); (c) in-situ process particle size sensors (in-line or at line, e.g., FOQELS); (d) routine measurement of particle shape and structure (e.g., image analysis). Instrumental methods resulting from these activities are discussed in terms of measurement principles and the strengths and weaknesses of these methods for characterizing PSDs. Business and societal driving forces will impact customer perceived instrumentation and knowledge needs for the 21st century and the ability to meet the specific difficult technical challenges in particle size distribution characterization mentioned above. Anticipated progress toward meeting these technical challenges is discussed in conjunction with the associated anticipated advances in required technologies.

Heard on the hotline

Powder-based additive manufacturing (AM) technologies are capable of producing high-performance parts for a diverse set of applications. Many AM processes require specific tolerances on feedstock powder to be successful, necessitating high-quality, precise measurements of particle size distribution (PSD) and morphology. This study investigates the use of several powder PSD and morphology measurement methods—including laser diffraction (LD)-based and static image analysis (SIA) techniques—for AM applications. Four AM powders of varying size and shape were tested to compare the influence of data collection methodology on reported powder characteristics. Results indicate that a disparity exists between the PSD and powder morphology data collected using the three method types evaluated, particularly when measuring fine and irregular powder particles. Considering the advantages and disadvantages of both SIA and LD-based methods, consistent use of methodological approach is recommended when analyzing powder PSD and morphology for AM applications.