Pellet Size Distribution Research Articles

Experimental Research on Breakage Characteristics of Feed Pellets under Different Loading Methods

Particle breakage is a common phenomenon during the processes of production, storage, and transportation. Because of the requirements for pellet integrity in poultry farming, research on the breakage characteristics of feed pellets is necessary. In this paper, repeated compression tests under different loading forces and repeated impact tests under different air pressures were carried out with feed pellets as the research object. The breakage behaviors were described, and the particle size distribution of feed pellets was analyzed quantitatively. The results revealed a positive correlation between crack density in feed particle beds and loading force. The compression process was divided into three stages based on force–displacement curves. The size of the feed pellets during repeated impacts decreased continuously and was negatively correlated with air pressure. The Weibull function accurately described the particle size distribution, with R2 values exceeding 0.97 and 0.96. The Weibull parameters showed a steady breakage degree in compression tests and a growing breakage degree in impact tests. The variation in energy and pulverization rate under different loading conditions was examined as the number of loading cycles increased. The relationship between energy and pulverization rates was fitted, showing that both parameters increased with loading cycles in different loading methods. The model of Vogel and Peukert could describe the relationship between energy and pulverization rate well, with R2 values exceeding 0.94. The minimum energy required for pellet breakage was higher in compression than in impact due to the compaction of the feed particle bed during repeated compression. The results can provide basic theory and data support for breakage characteristics and quality evaluation of feed pellets.

Online Monitoring of Iron Ore Pellet Size Distribution Using Lightweight Convolutional Neural Network

An improved U-Net based convolutional neural network (CNN) is proposed in this paper for online size monitoring of iron ore pellets. The proposed CNN model is a lightweight version of U-Net. In the proposed model each convolution block of the encoder subnet consists of a concatenated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$</tex-math> </inline-formula> filter kernel, a batch normalization layer, and a ReLU nonlinearity. The proposed CNN model efficiently detects the pellets even under variable illumination conditions and images with overlapping pellets. The proposed model is evaluated using images of iron ore pellets acquired at the discharge end of a pelletizer disc. The proposed CNN model performs satisfactorily using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 39% less learnable parameters, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 16% reduced computational cost than the original U-Net model. The proposed model outperforms the conventional U-Net, different variants of U-Nets, FCN, and SegNet in weighted IoU, BF Score, global accuracy, SSIM, and PSNR. Experimental validation also shows a good agreement between the obtained pellet size distribution and the manual sieving result. This makes the proposed algorithm suitable for online pellet size-distribution measuring instruments. This method provides a more accurate and fast size monitoring system for iron ore pellets, ideal for real-time industrial applications. The image dataset and source codes are available at the below given link. https://github.com/aryadeo/Pellet_Size_Distribution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the advent of digitalization in industries, several attempts are being made to use innovative industrial monitoring systems in steel manufacturing plants. Most of the pellet plants in steel industries use human supervision for pellet growth monitoring. In this paper, a camera-based pellet size distribution monitoring method is proposed. A novel deep learning-based algorithm is used for the detection of pellets from the captured image followed by computation of pellet size analysis. Due to low computational complexity and better accuracy, the proposed method is suitable for online pellet size distribution system development compared to other previously proposed methods. However, protection from the harsh industrial environment is required for the uninterrupted and continuous operation of the system. As a future scope, the obtained pellet size information can be used as one of the control variables in designing a control system for the automated operation of the pelletizer disc.

Error analysis of green pellet size distribution measurement on conveyors using simulation method

Error analysis of green pellet size distribution measurement on conveyors using simulation method

Evolution of biomass particles during pelletization process

Pulverizing is an essential unit operation in co-firing biomass with coal. Pulverizers are only compatible with pellet forms of fibrous biomass materials and crush them down to their original forming particle sizes. That is why the data on the size distribution of the particles forming a biomass pellet is crucial to achieving optimum combustion conditions. The current study determines the internal particle size distribution of pellets after wet disintegration, following ISO 17830 standard, and aims to suggest improvements to the mentioned standard based on new measured evidence. Experiments were carried out on white wood pellets (no bark) and brown wood pellets containing bark at four water temperatures: 20, 40, 60, and 95 °C, with or without stirring. The particle size distribution of the pre-pelletizer wood particles was also measured and compared with particles in the formed pellets. Ambient water temperature of 20 °C was found to be adequate for the complete disintegration of pellets, and no mechanical stirring was required. About 30% of particles in the disintegrated pellets were 0.5–1.0 mm. Pelletization changes the particle size distribution to smaller particles. The disintegrated bark pellets contained more fines than white pellets.

Applicability of image analysis to support QbD driven development of pellets

Objective The stages of preparing high drug loaded pellets were investigated using static and dynamic imaging techniques to provide a greater understanding and ease the scale up process. Significance An example of a real case laboratory and production scale quality by design (QbD) based development of pellets is demonstrated. Potential process analytical technology (PAT) approaches by dynamic image analysis (DIA) are presented in various process phases. Methods Pellets were prepared at laboratory and production scale (high shear granulation, extrusion/spheronization, drying, and coating). The influence of process parameters on pellet properties (aspect ratio (AR), yield, pellet size, and their distribution) was investigated using static and DIA. During coating, we focused on the coating thickness and identification of potential agglomeration. Results and conclusion The effects of kneading time, amount of water, extrusion screen plate (ESP) opening diameter and thickness on pellet properties were confirmed in accordance with literature. In terms of screw speed, spheronization speed and time, no considerable influence on pellet properties was observed in the range of studied process parameters, thereby confirming the design space. In addition to the ESP thickness and opening diameter, quality of the ESP impacts the pellet properties. Lastly, coating thickness measurements with dynamic and static image analysis were comparable and an exemplary case of in-line agglomeration detection was presented. Real-time evaluation with PATVIS APA is an effective PAT tool for the evaluation of spheronization (pellet size distribution, AR, and yield) and coating (coating thickness, agglomeration detection).

Online size distribution measurement of dense iron green pellets using an efficient and multiscale nested U-net method

Accurately measuring the pellet size distribution during the pelletizing process is critical, because the pellet size distribution is a key quality indicator and affects the efficiency of the blast furnace. In this study, a method based on a convolutional neural network is proposed to measure the pellet size distribution in the stable area of a rotary disc. The proposed network only uses simple convolution layers in sequence to realize multiscale performance; furthermore, it uses a bottleneck layer and a concatenate path to improve the computing efficiency and alleviate the vanishing gradient problem, respectively. Various experimental results demonstrate that the proposed network outperforms other comparison methods in segmenting overlapped pellets, especially pellets of different sizes. The pellet size distribution obtained by the proposed method agreed well with the manual sieving results. Moreover, the computing time of the proposed method can meet the requirements of online size distribution measurement.

Large-scale transportation and storage of wood pellets: Investigation of the change in physical properties

The change in physical properties of wood pellets, with a focus on particle size distributions due to pellet breakage and attrition, was studied in a large-scale (∼450ton/h) transportation system. Critical locations with a high probability of breakage through the whole transportation system were chosen and sampled to study the effect of transportation system design and operation on the mechanical properties of pellets. Bulk density, mechanical durability, moisture content, and particle size distribution of pellets were characterized for each sample. Analysis of variance showed that there were significant differences between the percentages of small particles (< 5.6mm) in the samples taken at different locations, especially at one with a vertical free fall of 7.8m. On average, this relatively long drop increased the proportion of particles < 5.6mm in the samples from 8.73% to 14.09%, and that of particles < 3.15mm from 4.82% to 9.01%. Moreover, the measurements showed a wide deviation in the mechanical durability values, between a minimum of 90.8% and a maximum of 98.7%, which were not correlated to the sampling points but related to pellet properties. It can be concluded that pellet transportation systems require more dedicated design strategies to prevent breakage and attrition.

Image processing for analysis of carbon black pellet size distribution during pelletizing: Carbon black PSD (PSD: pellet size distribution) by image processing

This paper describes a method for measuring carbon black pellet size distribution (PSD). Carbon black pellet size is related to mixing efficiency, working environment, and transport. Therefore, must be managed appropriately. The standard method ASTM D1511 to measure the pellet size requires too much time for real field analysis, and thus it is impossible to check the in-line pellet size. Therefore, if a pellet size issue occurs during operation, it cannot be addressed quickly. This paper presents a method for frequent, real-time carbon black PSD measurement through image processing. The processes for pellet imaging, image processing, PSD calculation, and original image processing correction are introduced in detail. A comparison of the ASTM 1511 and image analysis results was also performed. Finally, the in-line monitoring results analyzed by the image processing method are described. This study provides a more efficient and improved method to analyze carbon black PSDs.

Online monitoring of green pellet size distribution in haze-degraded images based on VGG16-LU-net and haze judgement

Online monitoring of pellet size distribution (PSD) of green pellets is an important work in product quality control of pelletization process. Conventionally, image segmentation technique is a preliminary step in computer vision-based PSD monitoring. However, haze, pellets overlapping, and uneven illumination contribute to the main challenges that severely impair the segmentation performance and PSD measurement accuracy. This article proposed a fully automatic online PSD monitoring method incorporating a K-means clustering-based haze judgment module, a lightweight U-net segmentation model with the fusion of none-weight VGG16 features (VGG16-LUnet), and a convex-hull detection and ellipse fitting model for adhesive pellet separation and contour fitting. The VGG16-LUnet model can accurately segment the pellets from both hazy and haze-free images with the help of haze judgment module. Thus, this model can be called VGG16-LUnet-TAdj. Then, a contour fitting model is applied to determine the pellets sizes based on the segmentation results, and the PSD is obtained as well. Extensive experiments on the segmentation of in situ captured green pellet images and the corresponding PSD curves demonstrate that our proposed method performs comparable or even favorable to the state-of-the-art methods.

Pilot scale production of fuel pellets from waste biomass leaves: Effect of milling size on pelletization process and pellet quality

This work investigates the effect of biomass milling size on pelletization process and pellet quality, using garden waste (a non-woody biomass feedstock). Three different milling sizes were considered, viz. ‘as received’ (>25.4 mm), coarse (25.4 mm), and fine (6.25 mm) at a moisture content of around 10% and a die size of 15 mm. The particle size distribution plots showed a significant difference in ‘median particle size diameter’ (D50) for all three cases. Nominal changes in pellet quality parameters (density, dimension, and durability) were observed during pilot scale pelletization with different milling sizes. However, the results showed an increased throughput capacity (29.5 to 60 kg h−1) with decrease in milling size from 'as received' to fine. In addition, the wet disintegration method was performed to analyze the internal particle size distribution of pellets. The results revealed that the difference in D50 values for the internal particle size of produced pellets was insignificant in above cases. Furthermore, the specific energy consumption value reduced from 141.2 to 100.2 kWh ton−1 by reducing biomass milling size from coarse to fine. The production cost reduced by 23% for fine feedstock as compared to coarse while producing pellets under given conditions.

An automated and continuous method for the optical measurement of wood pellet size distribution and the gravimetric determination of fines

This paper presents an automated, continuous method to determine wood pellet size distributions of larger and, hence, representative samples based on 2D-digital image analysis. The analysis routine developed combines several image processing steps to detect and separate the respective particles. The test unit designed allows length determination of up to 140 kg of pellets with a rate of 10.2 kg/h without any manual intervention. Pellets with a diameter up to 10 mm and a maximum length of about 50 mm can be analysed. In addition, the amount of fines is determined gravimetrically. Verification tests confirm an average standard deviation of 0.42% for the effective pellet length. First measurements of various pellet types show significant differences in their initial size distributions. The method developed provides a tool, which allows a more precise quality control of wood pellet size distribution.

Assessment of physical and dissolution characteristics of various itraconazole capsule formulations: a comparative analysis

&lt;p class="abstract"&gt;&lt;strong&gt;Background:&lt;/strong&gt; This &lt;em&gt;in vitro&lt;/em&gt; study compared physical parameters and the dissolution profile of innovator itraconazole capsule formulation, i-Tyza, and 5 other generic capsule formulations available in the Indian market.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Methods:&lt;/strong&gt; The number of pellets and size distribution were determined using naked eye examination and sieving method, respectively. Dissolution profile of formulations was done at 15, 30, 45, and 60 minutes, using a United States Pharmacopeia type II Paddle apparatus in simulated gastric fluid (SGF, pH 1.2) without enzymes, acetate buffer (pH 4.5) with 0.5% sodium lauryl sulfate (SLS), and phosphate buffer (pH 6.8) with 0.5% SLS.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Results:&lt;/strong&gt; All formulations had capsule size 0. Capsule fill weight (~335 to ~510 mg) and total pellet number (127 to 810) varied across formulation, with the innovator brand having the highest number of pellets. Innovator product and i-Tyza had similar fill weight (~460 mg). Pellet size distribution of the innovator product, brand 2, brand 3, and i-Tyza was relatively narrow. In SGF, except brand 1 (84% dissolved) and brand 5 (80% dissolved), all the formulations had near-complete (&amp;gt;85% drug dissolved) or complete dissolution (&amp;gt;90% drug dissolved) at 60 minutes. In acetate buffer, pH 4.5 with 0.5% SLS and phosphate buffer, pH 6.8 with 0.5% SLS, only the innovator product and i-Tyza demonstrated near-complete to complete dissolution at 60 minutes (96% and 90% dissolved).&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; Across all the itraconazole generic formulations evaluated, i-Tyza had comparable physical characteristics and dissolution profile to the innovator product. The &lt;em&gt;in vitro&lt;/em&gt; dissolution profile of i-Tyza may indicate adequate &lt;em&gt;in vivo&lt;/em&gt; performance.&lt;/p&gt;

Sensitivity of a continuous hot-melt extrusion and strand pelletization line to control actions and composition variation

The purpose of this work was to develop a robust hot-melt extrusion and strand pelletization process for manufacturing pellets with an immediate release (IR) of a poorly water-soluble active pharmaceutical ingredient (API), nimodipine. The robustness of pharmaceutical continuous manufacturing processes and of its control strategy is vital for competitiveness to traditional batch-manufacturing. Therefore, first the sensitivity of product quality, process stability, and process monitoring tools to i) parameter changes due to control actions and ii) typical process deviations, i.e., feeding errors, was investigated in a design of experiments (DoE). Thereby, die melt pressure was found to be highly sensitive to composition deviations, i.e. a limiting factor for process stability. Especially critical were deviations to increased HPMC content, since it behaved as a filler in the melt. Pelletization, or pellet size and size distribution respectively, were found to be sensitive to an increased throughput, due to the resulting insufficient strand cooling before the pelletizer. API dissolution in contrast, was found to be robust across the entire investigated range of formulation and process settings. Second, a design space for the production of IR pellets for subsequent tableting was established, and finally, a technical control strategy was developed to ensure a robust process. Near-infrared (NIR) spectroscopy was applied to monitor API content and the sensitivity of the residence time distribution (RTD) was investigated by means of tracer measurements. NIR-based API content monitoring and RTD models for material tracking were found to be at risk after processing melt with high HPMC content, due to a lack of purging by less viscous formulation compositions.

An image-based method for online measurement of the size distribution of iron green pellets using dual morphological reconstruction and circle-scan

Particle size distribution (PSD) of green pellets is critically important in pelletizing process because it greatly affects the quality of iron ore oxidized pellet as well as the efficiency of blast furnace process. Image-based methods are effective in automatic measurement of PSD, but are limited in their capacity in segmenting overlapped pellets and computation efficiency. To overcome these shortcomings, a novel image segmentation algorithm is proposed in the present work, with which overlapped pellets in the image can be well separated and thus the PSD of iron green pellets can be measured online with good accuracy. The proposed algorithm first identifies the markers of each pellet directly from the greyscale image by a dual morphological reconstruction method, then a circle-scan method is proposed to split the overlapped pellets and measure the diameter of each segmented pellets. The proposed methods were verified by experiments and compared with methods available in literature as well as manual sieving results. It was demonstrated that the proposed algorithm achieves better segmentation performance and has an accuracy of 94.3%. The proposed measuring system has already been tested in a local steel company. Results show that the robustness and computing efficiency of the proposed image-based method is sufficient to satisfy the requirement for online PSD measurement in harsh environment of the pelletizing process.

Detection and segmentation of iron ore green pellets in images using lightweight U-net deep learning network

In steel manufacturing industry, powdered iron ore is agglomerated in a pelletizing disk to form iron ore green pellets. The agglomeration process is usually monitored using a camera. As pellet size distribution is one of the major measures of product quality monitoring, pellets detection and segmentation from the image are the key steps to determine the pellet size. Traditional image processing algorithms are not only challenged by the complicated constitution of pellets, sediment and residuals in the image, but also by the harsh and unbalanced light reflection on the pellet centrum area and the background which results in tedious parameter adjustment work and pool performance. To solve these problems, we design a lightweight U-net deep learning network to automatically detect pellets from images and to obtain the probability maps of pellet contours. Compared to classic U-net, the proposed network has fewer parameters and introduces batch normalization layers, which greatly reduces the computing time and improves generalization ability of the network. A concentric circle model is then used to separate clumped contours of the pellets, and the pellets shapes are detected via ellipse fitting. The proposed method is verified using images captured from an industrial pelletizing disk, and its performance is compared with traditional methods and the classic U-net. Results show that the proposed method achieves better segmentation performance in DICE and ROC indexes and shows good robustness to uneven illumination. Tests on temporal image sequences demonstrate that the proposed method is effective in monitoring the pellet size distribution and the pellet shape as well. Results of this work have potential usage in online detection of iron ore green pellets and other types of particles.

Gellan gum and polyvinylpyrrolidone (PVP) as binding agents in extrusion/spheronization pellet formulations.

The aim of this work was to evaluate gellan gum as binder in pellet formulations, with theophylline as the model drug, in comparison with polyvinylpyrrolidone (PVP). A full 32 factorial design was realized, with binder and diluent factors at three levels each. Pellets were produced by the extrusion/spheronization technique, and dried in a fluid-ized bed. Physical tests and dissolution tests were conducted. The results showed that the binder factor was not significant for pellet size and granulometry distribution. Rather, trends of a different response of gellan gum were identified, in comparison with PVP, in aspect ratio and dissolution tests: more round pellets were obtained in formulations with gellan gum, and more variable dissolution resulted when this polysaccharide was present. Therefore, if the usage of this compound in immediate release pellet formulations is verified, this justifies the interest in the development of sustained release systems using gellan gum.

Image-based Method for Measuring Pellet Size Distribution in the Stable Area of Disc Pelletizer

Image-based Method for Measuring Pellet Size Distribution in the Stable Area of Disc Pelletizer

Influence of the dimensions of spheroniser plate protuberances on the production of pellets by extrusion-spheronisation

The influence of the dimensions of square-patterned pyramidal protuberances on a spheronisation plate on pellet yield, size and shape distributions, surface tensile strength and surface morphology was investigated using a 45wt% microcrystalline cellulose/water paste. Tests were conducted using four extrudate diameters (1.0, 1.5, 2.0 and 2.5 mm) generated by screen extrusion and seven plate geometries, including a flat plate as a control. Geometrical analyses of the protuberance shapes provide some insight into the observed differences. Sharper protuberances reduced yield (promoting breakage and attrition) but tended to give narrower size distributions and less even pellet surfaces. The pellet yield for 1 mm extrudates was also subject to losses caused by fragments passing through the 1.0 mm gap between the plate and the spheroniser wall. Pellet tensile strengths were noticeably greater for 1.0 mm diameter extrudates, which is attributed to the greater extensional strain imparted on the paste during the extrusion step. For some geometries there is an optimal ratio of extrudate to protuberance dimensions.

Automatic Image Processing based Size Characterization of Green Pellets

The size of pellets has a significant effect on the performance of blast furnaces. Based on experience, the universally accepted size of pellets for efficient blast furnace operations is between 9 millimeters to 16 millimeters. But presence of smaller size pellets or fines lowers the blast furnace stack permeability, increases dust losses and may lower the maximum permissible blast temperature for smooth operation of the furnace. Pellets too big are also undesirable, particularly if its reducibility is low and is poor in strength, thus undergoing physical degradation during furnace operation.This paper relates to a non-contact method of measuring the size of green pellets being fed into the Blast furnace. A method is developed for the automatic size characterization of green pellets in the conveyor belts before the pellets are being indurated in a travelling grate furnace to confer the required physical and metallurgical properties. The method employs imaging and illumination devices installed in the conveyor belt in the pellet plant of Tata Steel and then, processing those images using imaging algorithms to obtain the size distribution. Apart from the size distribution of the green pellets, the system also identifies the pelletizing disc from which the green balls originated so as to effectively control the pelletizing process.

An industrial image processing-based approach for estimation of iron ore green pellet size distribution

Green pellets are spherical objects mainly made from crushed iron and water in rotating pelletizing disk. Pellets play an important role in the process of direct reduction steel manufacturing. For high quality steel production, pellets should be of an appropriate size. Oversized pellets cannot be properly cooked in furnace and undersized pellets may pass through metal grid of the furnace, which affects the performance of the process. In most of the steel manufacturing companies, operators use sequential sampling and traditional methods to measure size of pellets, however, limited number of tested pellets, low speed of the quality control and human errors are the most common drawbacks of this method. A solution to these shortcomings is continuous monitoring and accurate measurement through a fully-automatic approach. In this paper, a method using image processing technique is proposed and verified for estimating size distribution of iron-ore green pellets. For segmentation of single and multiple pellets in images captured from live video, we have utilized morphological methods, watershed algorithm, and linear searching. Then, a Support Vector Machine (SVM) is employed for classification of segmented pellets. The proposed method has already been implemented in Mobarakeh Steel Complex, where the method was tested on about 1000 sample pellets. Results show that the accuracy of method is 95.1% in detection of single pellet elements.