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.

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 β [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 β = 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.

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.

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.

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.

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.

Cleaning and Disinfection of Biofilms Composed of Listeria monocytogenes and Background Microbiota from Meat Processing Surfaces

Surfaces of food processing premises are exposed to regular cleaning and disinfection (C&D) regimes, using biocides that are highly effective against bacteria growing as planktonic cells. However, bacteria growing in surface-associated communities (biofilms) are typically more tolerant toward C&D than their individual free-cell counterparts, and survival of pathogens such as Listeria monocytogenes may be affected by interspecies interactions within biofilms. In this study, Pseudomonas and Acinetobacter were the most frequently isolated genera surviving on conveyor belts subjected to C&D in meat processing plants. In the laboratory, Pseudomonas, Acinetobacter, and L. monocytogenes dominated the community, both in suspensions and in biofilms formed on conveyor belts, when cultures were inoculated with eleven-genus cocktails of representative bacterial strains from the identified background flora. When biofilms were exposed to daily C&D cycles mimicking treatments used in food industry, the levels of Acinetobacter and Pseudomonas mandelii diminished, and biofilms were instead dominated by Pseudomonas putida (65 to 76%), Pseudomonas fluorescens (11 to 15%) and L. monocytogenes (3 to 11%). The dominance of certain species after daily C&D correlated with high planktonic growth rates at 12°C and tolerance to C&D. In single-species biofilms, L. monocytogenes developed higher tolerance to C&D over time, for both the peracetic acid and quaternary ammonium disinfectants, indicating that a broad-spectrum mechanism was involved. Survival after C&D appeared to be a common property of L. monocytogenes strains, as persistent and sporadic subtypes showed equal survival rates in complex biofilms. Biofilms established preferentially in surface irregularities of conveyor belts, potentially constituting harborage sites for persistent contamination.IMPORTANCE In the food industry, efficient production hygiene is a key measure to avoid the accumulation of spoilage bacteria and eliminate pathogens. However, the persistence of bacteria is an enduring problem in food processing environments. This study demonstrated that environmental bacteria can survive foam cleaning and disinfection (C&D) at concentrations used in the industrial environment. The phenomenon was replicated in laboratory experiments. Important characteristics of persisting bacteria were a high growth rate at low temperature, a tolerance to the cleaning agent, and the ability to form biofilms. This study also supports other recent research suggesting that strain-to-strain variation cannot explain why certain subtypes of Listeria monocytogenes persist in food processing environments while others are found only sporadically. The present investigation highlights the failure of regular C&D and a need for research on improved agents that efficiently detach the biofilm matrix.

Acoustic methods for high-throughput protein crystal mounting at next-generation macromolecular crystallographic beamlines

To take full advantage of advanced data collection techniques and high beam flux at next-generation macromolecular crystallography beamlines, rapid and reliable methods will be needed to mount and align many samples per second. One approach is to use an acoustic ejector to eject crystal-containing droplets onto a solid X-ray transparent surface, which can then be positioned and rotated for data collection. Proof-of-concept experiments were conducted at the National Synchrotron Light Source on thermolysin crystals acoustically ejected onto a polyimide `conveyor belt'. Small wedges of data were collected on each crystal, and a complete dataset was assembled from a well diffracting subset of these crystals. Future developments and implementation will focus on achieving ejection and translation of single droplets at a rate of over one hundred per second.