Delay Compensation in a Feeder-Conveyor System Using the Smith Predictor: A Case Study in an Iron Ore Processing Plant.

Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation.

The Role of Reduced Graphene Oxide in Enhancing the Mechanical and Thermal Properties of a Rubber Cover Joint.

The development of high-performance rubber composites has always been a research hotspot in the field of conveyor belt manufacturing. In this work, a rubber cover joint composite made of reduced graphene oxide (rGO) was prepared using latex mixing and mechanical blending methods, with a steel wire rope conveyor belt as the research object, and the influence of the rGO content on the properties of the rubber composite is discussed. The structure and morphology characterization of the rGO/NR rubber show that the addition of rGO does not change its crystal structure, and 1.2 phr rGO is uniformly dispersed throughout the rubber composite. As more rGO is added, the mechanical properties of the rGO rubber cover joint first improve and then worsen. With the addition of 1.2 phr, the cross-linking density increases by 80.6%, the tensile strength of the rubber composites increases by 49.7%, the elongation at break increases by 23.6%, and the adhesion strength increases by 12.4%. The tensile strength of the rGO rubber cover joint can still maintain 72.5% of its pre-thermal aging value. The wear resistance and thermal conductivity increase as more phr is added. When 3.0 phr is added, the wear resistance of the rubber composites increases by 32.9%, the thermal conductivity increases by 118.8%, and the temperature difference at the completion of vulcanization decreases from 4.5 °C to 1.8 °C. The results show that when 1.2 phr of rGO is added, the rubber conveyor belt joint obtains the best comprehensive performance. These enhanced comprehensive properties allow for the practical application of rGO nanomaterials to conveyor belt rubber.

Lymph node medulla regulates the spatiotemporal unfolding of resident dendritic cell networks.

Unlike macrophage networks composed of long-lived tissue-resident cells within specific niches, conventional dendritic cells (cDCs) that generate a 3D network in lymph nodes (LNs) are short lived and continuously replaced by DC precursors (preDCs) from the bone marrow (BM). Here, we examined whether specific anatomical niches exist within which preDCs differentiate toward immature cDCs. In situ photoconversion and Prtn3-based fate-tracking revealed that the LN medullary cords are preferential entry sites for preDCs, serving as specific differentiation niches. Repopulation and fate-tracking approaches demonstrated that the cDC1 network unfolded from the medulla along the vascular tree toward the paracortex. During inflammation, collective maturation and migration of resident cDC1s to the paracortex created discontinuity in the medullary cDC1 network and temporarily impaired responsiveness. The decrease in local cDC1 density resulted in higher Flt3L availability in the medullary niche, which accelerated cDC1 development to restore the network. Thus, the spatiotemporal development of the cDC1 network is locally regulated in dedicated LN niches via sensing of cDC1 densities.

FASTory assembly line power consumption data.

Machine learning (ML) techniques are widely adopted in manufacturing systems for discovering valuable patterns in shopfloor data. In this regard, machine learning models learn patterns in data to optimize process parameters, forecast equipment deterioration, and plan maintenance strategies among other uses. Thus, this article presents the dataset collected from an assembly line known as the FASTory assembly line. This data contains more than 4,000 data samples of conveyor belt motor driver's power consumption. The FASTory assembly line is equipped with web-based industrial controllers and smart 3-phase energy monitoring modules as an expansion to these controllers. For data collection, an application was developed in a timely manner. The application receives a new data sample as JavaScript Object Notation (JSON) every second. Afterwards, the application extracts the energy data for the relevant phase and persists it in a MySQL database for the purpose of processing at a later stage. This data is collected for two separate cases: static case and dynamic case. In the static case, the power consumption data is collected under different loads and belt tension values. This data is used by a prognostic model (Artificial Neural Network (ANN)) to learn the conveyor belt motor driver's power consumption pattern under different belt tension values and load conditions. The data collected during the dynamic case is used to investigate how the belt tension affects the movement of the pallet between conveyor zones. The knowledge obtained from the power consumption data of the conveyor belt motor driver is used to forecast the gradual behavioural deterioration of the conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems.

Evaluating the effect of temperature and multiple bends on an automated pork belly firmness conveyor belt classification system.

Skin-on, and bone-in bellies (n = 94) were cut into Canadian specifications and assessed on an automated conveyor belt system based on different levels of firmness. Temperature settings at 4 °C, 2 °C, and - 1.5 °C had significant effect (P < 0.05) on the bending angle, after 24 cm of the belly had passed the nosebar. The stepwise regression relationship had R2 âˆ¼ 0.18-0.67 between iodine value and bending angle at all temperatures. Bending bellies multiple times changed firmness classification of bellies at 4 and 2 °C, but bend number did not influence firmness classification at -1.5 °C. The automated conveyer belt system presented the potential to classify pork bellies based on firmness for industrial applications.

Enhancing Natural Rubber Tearing Strength by Mixing Ultra-High Molecular Weight Polyethylene Short Fibers.

Rubber products generally need to have high resistance to abrasion, tear, and cutting. Filling short fiber with strong mechanical properties and forming a net in the rubber matrix is a good method to realize the above aims. In this article, ultra-high molecular weight polyethylene (UHMWPE) short fibers with a diameter of 20 µm and a length of 2 cm were filled into natural rubber (NR) to improve the tear strength of the NR. The influence of the short fiber mass fraction and vulcanization conditions on the mechanical properties of the composites were investigated. The results show that the milling process and vulcanization conditions are key factors in enhancing tear resistance performance. Double-roll milling and vulcanization at 143 °C for 40 min result in strong interfacial adhesion between the UHMWPE short fibers and the NR. The addition of 2 phr of UHMWPE fiber increases the tear strength of the composite material by up to 150.2% (from 17.1 kN/m to 42.8 kN/m) while also providing excellent comprehensive performance. Scanning electron microscope (SEM) imaging confirmed that the UHMWPE short fibers are dispersed in the NR matrix homogeneously, and the interface is close and compact. As a control experiment, UHMWPE resin powder was directly filled into the NR, and then the composite was vulcanized using the same process as that used for the NR/UHMWPE short fiber composite. The results show that the mechanical strength of the NR/resin powder composite exhibits minor improvement compared with NR. As there is no complicated surface modification of the UHMWPE fiber, the results reported may be helpful in improving the tear resistance of the industrially prepared rubber conveyor belts.

Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis.

For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA.

A Laboratory Device Designed to Detect and Measure the Resistance Force of a Diagonal Conveyor Belt Plough.

This paper presents a laboratory device simulating a section of a conveyor belt on which a diagonal plough is installed. Experimental measurements were carried out in a laboratory belonging to the Department of Machine and Industrial Design at the VSB-Technical University of Ostrava. During the measurements, a plastic storage box, representing a piece load, transported on the surface of a conveyor belt at a constant speed was brought into contact with the front surface of a diagonal conveyor belt plough. The aim of this paper is to determine the amount of resistance generated by the diagonal conveyor belt plough when it is placed at different angles of inclination ß [deg] in relation to the longitudinal axis, based on the experimental measurements performed using a laboratory measuring device. Based on the measured values of tensile force required to keep the conveyor belt moving at a constant speed, the resistance to the conveyor belt movement is expressed, with a value of 20.8 ± 0.3 N being attained. Based on the ratio of the measured value of the arithmetic average of the resistance force and the weight of the used length of the conveyor belt, a mean value of the specific movement resistance of the size 0.33 [N·N - 1] is calculated. This paper presents the time records obtained by measuring the tensile forces, on the basis of which it is possible to determine the magnitude of the force. The resistance during the ploughing operation of the diagonal plough when acting on a piece load placed on the working surface of the conveyor belt is presented. From the measured values of tensile forces presented in the tables, this paper reports the calculated values of the friction coefficient obtained during the movement of the diagonal plough when moving a piece of load with the defined weight from the working surface of the relevant conveyor belt. The maximum value of the arithmetic mean for the friction coefficient in motion µ = 0.86 was measured at an inclination angle of the diagonal plough of ß = 30 deg.

CMOS Detector Staggered Array Module for Sub-Terahertz Imaging on Conveyor Belt System.

A complementary metal-oxide-semiconductor (CMOS) detector array is proposed to improve the sub-terahertz imaging resolution for objects in the conveyor belt system. The image resolution is limited to the implemented configuration, such as the wide spacing in the detector array, the high conveyor belt speed, and the slow response of the signal conditioning block. The proposed array can improve the image resolution in the direction perpendicular to the movement of the belt, which is determined by the size and interval of the detector pixel, by configuring the array into two replaceable columns located at the misaligned horizontal positions. Replaceable detector unit pixels are individually attached to the motherboard after measuring and evaluating the detection performance to construct the proposed array. The intensities of 32 detector pixels placed under the conveyor belt with a width of 160 mm were initially calibrated in every image, including the beam pattern of 0.2 THz signals generated from the gyrotron. The image resolution of the perpendicular direction obtained from the proposed array was measured to be approximately 5 mm at a conveyor belt speed of 16 mm/s, demonstrating a 200% improvement in resolution compared to the conventional linear array under the same conditions.

Surface Evaluation of Gyroid Structures for Manufacturing Rubber-Textile Conveyor Belt Carcasses Using Micro-CT.

Gyroid structures are among the most widely used three-dimensional elements produced by various additive manufacturing technologies. This paper focuses on a metrological analysis of Flexfill 92A material specimens with a relative density (25 to 85%) using industrial computer tomography. The results show that for a given structure, the best method is to use surface determination with the closure of internal defects in the material. The analysis implies that the smallest deviations of the specimens' external dimensions were achieved with respect to the CAD model at the highest relative densities. The wall thickness shows the smallest percentage change of 0.5685 at 45% relative density and the largest at 25% and 85% relative density. The nominal-actual comparison of manufactured specimens to the CAD model shows the smallest cumulative deviation of 0.209 mm at 90% and 25% relative density, while it slightly increases with increasing relative density. All produced specimens have a smaller material volume than their theoretical volume value, while the percentage change in volume is up to 8.6%. The surface of specimens is larger compared with the theoretical values and the percentage change reaches up to 25.3%. The percentage of pores in the specimens increases with increasing relative density and reaches 6%. The acquired knowledge will be applied in the framework of research focused on the possibilities of using additive manufacturing to produce a skeleton of rubber-textile conveyor belts. This paper presents initial research on the possibility of replacing the carcass of rubber-textile belts with an additive technology use.

Olive Fruit Selection through AI Algorithms and RGB Imaging.

(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

Human neutrophil kinetics: a call to revisit old evidence.

The half-life of human neutrophils is still controversial, with estimates ranging from 7-9 h to 3.75 days. This debate should be settled to understand neutrophil production in the bone marrow (BM) and the potential and limitations of emergency neutropoiesis following infection or trauma. Furthermore, cellular lifespan greatly influences the potential effect(s) neutrophils have on the adaptive immune response. We posit that blood neutrophils are in exchange with different tissues, but particularly the BM, as it contains the largest pool of mature neutrophils. Furthermore, we propose that the oldest neutrophils are the first to die following a so-called conveyor belt model. These guiding principles shed new light on our interpretation of existing neutrophil lifespan data and offer recommendations for future research.

Effect of Vulcanization Process Parameters on the Tensile Strength of Carcass of Textile-Rubber Reinforced Conveyor Belts.

Textile-reinforced conveyor belts are most widely used in various industries, including in the mining, construction, and manufacturing industries, to transport materials from one place to another. The conveyor belt's tensile strength, which primarily relies on the property of the carcass, determines the area of application of the belt. The main aim of the current work was to investigate the influence of vulcanization temperature and duration of the vulcanization process on the tensile properties of the carcass part of the conveyor belt. An extensive experiment was carried out on the tensile properties of woven fabrics that were intended to reinforce conveyor belts by aging the fabrics at the temperature of 140 °C, 160 °C, and 220 °C for six and thirty-five minutes of aging durations. Afterward, the textile-reinforced conveyor belts were produced at vulcanization temperatures of 140 °C, 160 °C, and 220 °C for six and thirty-five minutes of vulcanizing durations. The influence of the vulcanization process parameters on the tensile property of fabrics utilized for the reinforcement of the conveyor belt was analyzed. In addition, the effect of the dipping process of woven fabric in resorcinol-formaldehyde-latex on the tensile property of polyester/polyamide 66 woven fabric (EP fabric) was investigated. The investigation results revealed that the tensile strength of the carcass of the conveyor belt was significantly affected by vulcanization temperature. The conveyor belt vulcanized at 160 °C for 35 min has shown the optimum tensile strength, which is 2.22% and 89.06% higher than the samples vulcanized at 140 °C and 220 °C for 35 min, respectively. Furthermore, the tensile strength and percentage elongation at break of conveyor belts vulcanized at 220 °C were almost destroyed regardless of the vulcanization duration.

Use of an Ecosystem-Based Approach to Shed Light on the Heterogeneity of the Contamination Pattern of Listeria monocytogenes on Conveyor Belt Surfaces in a Swine Slaughterhouse in the Province of Quebec, Canada.

The role of the accompanying microbiota in the presence of Listeria monocytogenes on meat processing surfaces is not yet understood, especially in industrial production conditions. In this study, 300 conveyor belt samples from the cutting room of a swine slaughterhouse were collected during production. The samples were subjected to the detection of L. monocytogenes. Recovered strains were characterized by serogrouping-PCR, InlA Sanger sequencing and for their ability to form biofilm. A selection of isolates was compared with core genome multi-locus sequence typing analysis (cgMLST). The sequencing of the V4 region of the 16S RNA gene of the microorganisms harvested from each sample was carried out in parallel using the Illumina MiSeq platform. Diversity analyses were performed and MaAsLin analysis was used to assess the link between L. monocytogenes detection and the surrounding bacteria. The 72 isolates collected showed a low genetic diversity and important persistence characteristics. L. monocytogenes isolates were not stochastically distributed on the surfaces: the isolates were detected on three out of six production lines, each associated with a specific meat cut: the half carcasses, the bostons and the picnics. MaAsLin biomarker analysis identified the taxa Veillonella (p ≤ 0.0397) as a bacterial determinant of the presence of L. monocytogenes on processing surfaces. The results of this study revealed a heterogenous contamination pattern of the processing surfaces by L. monocytogenes and targeted a bacterial indicator of the presence of the pathogen. These results could lead to a better risk assessment of the contamination of meat products.

Long-Exposure RGB Photography with a Fixed Stand for the Measurement of a Trajectory of a Dynamic Impact Device in Real Scale.

The present manuscript proposes a novel method for the measurement of a trajectory of a falling impact hammer in the dynamic loading of conveyor belts and the determination of their impact resistance. The proposed method has been experimentally tested and the results of the measurements are presented in this manuscript. The proposed method is based on the long-exposure photography with a long-duration opened shutter of the Nikon D5000 DSLR camera. Results of the experimental research were compared with direct reference measurements performed using the L-GAGE LT3 laser distance sensor. Differences between values, obtained by the new method and by the reference measurements were up to ±3 mm. The standard deviation identified in all the experiments was 1 mm.

Exploring the Microdiversity Within Marine Bacterial Taxa: Toward an Integrated Biogeography in the Southern Ocean.

Most of the microbial biogeographic patterns in the oceans have been depicted at the whole community level, leaving out finer taxonomic resolution (i.e., microdiversity) that is crucial to conduct intra-population phylogeographic study, as commonly done for macroorganisms. Here, we present a new approach to unravel the bacterial phylogeographic patterns combining community-wide survey by 16S rRNA gene metabarcoding and intra-species resolution through the oligotyping method, allowing robust estimations of genetic and phylogeographic indices, and migration parameters. As a proof-of-concept, we focused on the bacterial genus Spirochaeta across three distant biogeographic provinces of the Southern Ocean; maritime Antarctica, sub-Antarctic Islands, and Patagonia. Each targeted Spirochaeta operational taxonomic units were characterized by a substantial intrapopulation microdiversity, and significant genetic differentiation and phylogeographic structure among the three provinces. Gene flow estimations among Spirochaeta populations support the role of the Antarctic Polar Front as a biogeographic barrier to bacterial dispersal between Antarctic and sub-Antarctic provinces. Conversely, the Antarctic Circumpolar Current appears as the main driver of gene flow, connecting sub-Antarctic Islands with Patagonia and maritime Antarctica. Additionally, historical processes (drift and dispersal limitation) govern up to 86% of the spatial turnover among Spirochaeta populations. Overall, our approach bridges the gap between microbial and macrobial ecology by revealing strong congruency with macroorganisms distribution patterns at the populational level, shaped by the same oceanographic structures and ecological processes.

Population dynamics of coliforms in a commercial Cheddar cheese production facility.

The detection of coliforms in young cheese is a potential indication of undesirable microbial growth within the processing environment. The aim of this study was to investigate sources and conditions that lead to the intermittent detection of coliforms (1-3 log cfu/g) in young Cheddar cheese at a single commercial facility. Analysis of historical production data, in combination with iterative investigative sampling events, was performed to determine coliform levels in milk, whey, curd, and surfaces at the beginning, middle, and end of the production day. After sanitation, conveyor belt pieces from the draining and matting conveyor (DMC) were collected and evaluated for bacterial survivors using culture-based methods and scanning electron microscopy. Production data analysis indicated that cheese produced later in the production day (≥16 h) was significantly more likely to test positive for coliforms than cheese made earlier in the production day (<12 h). Enumeration of coliforms in raw and heat-treated milk demonstrated that the subpasteurization thermal treatment (67-70°C, 26-28 s) was effective at reducing, but not eliminating, coliforms. Repeated sampling identified the DMC, particularly the drain belt and belt 1, as a critical area that supported coliform growth during the production day. Coliform levels in whey entering the weir maintained a level of <1 cfu/mL throughout production; however, coliform levels in whey below the drain belt increased from <1 cfu/mL at midday (8 h) to 5.04 log cfu/mL by the end of the production day (~18 h). Routine sanitation inside the DMC resulted in undetectable coliform levels on easily accessible surfaces. However, enrichment and scanning electron microscopy of belt sections revealed pockets of viable coliforms and other bacteria in cracks and defects in conveyor belts, indicating that sanitation did not eliminate all viable bacteria. Low levels of coliforms are present in heat-treated milk and survive sanitation in the DMC and could serve as the initial seed for high levels of coliforms at the end of the production day.

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks.

Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.

An Ore Image Segmentation Method Based on RDU-Net Model.

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry.

Frequent and accurate inspections of industrial components and equipment are essential because failures can cause unscheduled downtimes, massive material, and financial losses or even endanger workers. In the mining industry, belt idlers or rollers are examples of such critical components. Although there are many precise laboratory techniques to assess the condition of a roller, companies still have trouble implementing a reliable and scalable procedure to inspect their field assets. This article enumerates and discusses the existing roller inspection techniques and presents a novel approach based on an Unmanned Aerial Vehicle (UAV) integrated with a thermal imaging camera. Our preliminary results indicate that using a signal processing technique, we are able to identify roller failures automatically. We also proposed and implemented a back-end platform that enables field and cloud connectivity with enterprise systems. Finally, we have also cataloged the anomalies detected during the extensive field tests in order to build a structured dataset that will allow for future experimentation.