Search Results (19)

This study evaluates sensor-based pre-concentration using XRT technology as an alternative to selective mining for low-grade European copper ores (0.48% Cu), addressing the need for sustainable beneficiation amid declining ore grades and environmental pressures in green mining initiatives. Copper ore samples from Złote Hory mine (Czech Republic) were selectively extracted, mixed (1:1:1 ore 8–16 mm/ore 16–32 mm/waste rock 8–32 mm), and analyzed on Comex’s LSX-MAX laboratory sorter with dual-energy XRT sensors, calibrated for maximum product recovery via density-based classification into High-Density (product) and Low-Density (waste) fractions. Sorting achieved a 1:1 product-to-waste mass split from feed (Cu = 0.5%, 100% mass), yielding pre-concentrate at 0.91% Cu (52.08% mass yield, 95.67% recovery) and waste at 0.04% Cu (47.92% mass, 4.33% loss)—a 1.82x grade upgrade superior to mixed feed and 1.42x superior to selective mining (0.64% Cu at 66.21% yield). Combined approaches promise further optimization; future work will assess downstream grinding/flotation impacts, industrial scaling, and economic/environmental benefits. Full article

A study of 300 diamonds in the 2–4 mm size range revealed that X-ray transmission demonstrated a predictable relationship for detecting diamonds, with all diamonds being identified. In contrast, X-ray luminescence showed no consistent relationship between diamond characteristics and detection, and not all diamonds were identified using this method. When comparing the X-ray transmission response of diamonds to common gangue minerals found in dense media separation concentrates, X-ray transmission was found to incidentally detect small amounts of gangue particles. However, no such gangue detection occurred with X-ray luminescence, which responded only to diamonds. In pilot-scale tests, a belt-fed X-ray transmission sorter with a pressurized air ejection mechanism and a chute-fed X-ray luminescence sorter with a mechanical paddle ejection system were evaluated. The X-ray transmission sorter produced an average of 0.28 kg of concentrate per gram of diamonds separated, while the X-ray luminescence sorter generated 0.37 kg of concentrate per gram of diamonds separated. The X-ray transmission sorter achieved 99% diamond recovery, whereas the X-ray luminescence sorter achieved 91% diamond recovery. The higher concentrate mass obtained from the X-ray luminescence sorter is attributed to the ineffectiveness of the mechanical paddles, despite the superior contrast between gangue and diamonds in detection. Full article

Density separation using a wet method is the standard technique for extracting microplastics (MPs) from coastal sediments. However, the 2021 Japanese submarine volcanic eruption introduced substantial pumice into these sediments, complicating the process. Pumice contamination in the floating matter from density separation significantly increases the workload of visual sorting. Pumice, distinguished by its spherical shape and hardness, exhibits distinct rolling and bouncing behaviors compared to plastic. In this study, we evaluated the sorting efficiency of a vibratory sorter in separating pumice from floating matter, comparing its performance with the existing methods. We analyzed the progressive behavior and the virtual sorting efficiency of single large- and medium-diameter particles using a vibrating plate and the actual sorting efficiency of mixed large-diameter particles. The maximum Newton’s efficiencies (η_max) for the virtual sorting of single large-diameter pumice and plastic ranged from 0.74 to 1.00, and for medium-diameter particles, from 0.74 to 0.97. Sorting efficiency decreased with finer particles. The η_max for the actual sorting of mixed large-diameter pumice and plastic was between 0.68 and 1.00, lower than the virtual sorting efficiency. While vibratory sorting, based on Newton’s efficiency, does not replace visual sorting, the time required for vibratory sorting is 21% of that required for visual sorting, making it valuable for estimating approximate MP quantities in coastal sediments. Additionally, this study provides a practical method for beach cleanups. Full article

This study focuses on the segmentation part in the development of a potato-sorting system that utilizes camera input for the segmentation and classification of potatoes. The key challenge addressed is the need for efficient segmentation to allow the sorter to handle a higher volume of potatoes simultaneously. To achieve this, the study employs a region-based convolutional neural network (R-CNN) approach for the segmentation task, while trying to achieve more precise segmentation than with classic CNN-based object detectors. Specifically, Mask R-CNN is implemented and evaluated based on its performance with different parameters in order to achieve the best segmentation results. The implementation and methodologies used are thoroughly detailed in this work. The findings reveal that Mask R-CNN models can be utilized in the production process of potato sorting and can improve the process. Full article

Automated sorting of sweetpotatoes is necessary to reduce labor dependence and costs that are significant at today’s sweetpotato packing sheds. Although optical sorters have been widely adopted in commercial packing lines for many horticultural commodities, there remains an unmet need to develop dedicated technology for the automated grading and sorting of sweetpotatoes. Sorting mechanisms are the critical component that physically segregates products according to quality grades determined by a machine vision or imaging system. This study presents the new engineering prototypes and evaluation of three different pneumatically powered mechanisms for sorting sweetpotatoes online. Among the three sorters, the sorting mechanism, which employs a linear air cylinder to drive a paddle directly striking products, achieved the best overall accuracy and repeatability of 98% and 96.8%, respectively, at conveyor speeds of 4–12 cm/s. The sorter based on a rotary actuator also delivered decent accuracy and repeatability of 97.9% and 95.6%, respectively. The best-performing sorting mechanism was integrated with a machine vision system that graded sweetpotatoes based on size and surface defect conditions to separate graded sweetpotatoes into three quality categories. The errors of 0–1% due to the sorting process were obtained at conveyor speeds of 4–12 cm/s, confirming the efficacy of the manufactured sorting mechanisms. There was a declining trend with the conveyor speed in the performance of the sorting mechanisms when evaluated either in a standalone or integrated configuration. The proposed sorting mechanisms that are simple in construction and operation and of low cost are useful for developing a more full-fledged sorting system. More research is needed to enhance sorting performance and conduct extensive tests at higher conveyor speeds for practical application. Full article

Plastics recycling is an important component of the circular economy. In mechanical recycling, the recovery of high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. In chemical recycling, certain types of plastics should be removed first as they negatively affect the process. Such sortation of plastic objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical for any sorting is the proper identification of the plastic type. Spectroscopy is used to this end, increasingly augmented by machine learning (ML) and artificial intelligence (AI). Recent developments in the application of ML/AI in plastics recycling are highlighted here, and the state of the art in the identification and sortation of plastic is presented. Commercial equipment for sorting plastic recyclables is identified from a survey of publicly available information. Automated sorting equipment, ML/AI-based sorters, and robotic sorters currently available on the market are evaluated regarding their sensors, capability to sort certain types of plastics, primary application, throughput, and accuracy. This information reflects the rapid progress achieved in sorting plastics. However, the sortation of film, dark plastics, and plastics comprising multiple types of polymers remains challenging. Improvements and/or new solutions in the automated sorting of plastics are forthcoming. Full article

The current study focuses on analyzing the impact of integrating an optical sorter in a seed-separation technological flow, in terms of increasing the quality of the maize seeds appropriate for sowing. The study showed that there are situations when the use of optical separation may result in a number of difficulties in removing a variable rate of good seeds from the raw mass, which can bring economic disadvantages. The identified issue encouraged the development of several flow assessment approaches in order to determine the problem’s essence and to develop the best strategy for action. The key finding was that the evaluated optical sorting equipment cannot eliminate impurities without also removing good seeds, resulting in every 1% increase in impurity level and a rate of 0.70% of the good seeds lost. Therefore, farmers must carefully consider the scenarios where integrating optical sorting into their technological flow is a suitable option, considering the input material quality, the selling price of the product, and the risk of missing an important quantity of high-quality seeds. The working method described may be of significant importance to other farmers who intend to choose the components of grain-cleaning processes effectively. Full article

Alginate microcapsules are one of the attractive non-invasive platforms for handling individual cells and clusters, maintaining their isolation for further applications such as imaging cell sorter and single capsule qPCR. However, the conventional cell encapsulation techniques provide huge numbers of unnecessary empty homogeneous alginate microcapsules, which spend an excessive majority of the machine time on observations and analysis. Here, we developed a simple alginate cell encapsulation method to form content size-dependent alginate microcapsules to eliminate empty microcapsules using microcapillary centrifugation and filtration. Using this method, the formed calcium alginate microcapsules containing the HeLa cells were larger than 20m, and the other empty microcapsules were less than 3m under 4000 rpm centrifugation condition. We collected cell-containing alginate microcapsules by eliminating empty microcapsules from the microcapsule mixture with simple one-step filtration of a 20 m cell strainer. The electrical surface charge density and optical permeability of those cell-encapsulated alginate microcapsules were also evaluated. We found that the surface charge density of cell-encapsulated alginate microbeads is more than double that of cells, indicating that less voltage is required for electrical cell handling with thin alginate gel encapsulation of samples. The permeability of the alginate microcapsule was not improved by changing the reflective index of the medium buffer, such as adding alginate ester. However, the minimized thickness of the alginate gel envelope surrounding cells in the microcapsules did not degrade the detailed shapes of encapsulated cells. Those results confirmed the advantage of alginate encapsulation of cells with the centrifugation method as one of the desirable tools for imaging cell sorting applications. Full article

Color sorting is a technological operation performed with the aim of classifying compliant and noncompliant agricultural products in large-capacity industrial systems for agricultural product processing. This paper investigates the application of the YOLOv3 algorithm on raspberry images as a method developed for the detection, localization, and classification of objects based on convolutional neural networks (CNNs). To our knowledge, this is the first time a YOLO algorithm or CNN has been used with original images from the color sorter to focus on agricultural products. Results of the F1 measure were in the 92–97% range. Images in full resolution, 1024 × 1024, produced an average detection time of 0.37 s. The impact of the hyperparameters that define the YOLOv3 model as well as the impact of the application of the chosen augmentative methods on the model are evaluated. The successful classification of stalks, which is particularly challenging due to their shape, small dimensions, and variations, was achieved. The presented model demonstrates the ability to classify noncompliant products into four classes, some of which are appropriate for reprocessing. The software, including a graphic interface that enables the real-time testing of machine learning algorithm, is developed and presented. Full article

Recycling plastic is an important step towards a circular economy. Attaining high-quality recycled plastics requires the separation of plastic waste by type, color, and size prior to reprocessing. Automated technology is key for sorting plastic objects in medium- to high-volume plants. The current state of the art of commercial equipment for sorting plastic as well as challenges faced by Material Recovery Facilities (MRFs) to sort post-consumer plastics are analyzed here. Equipment for sorting plastic recyclables were identified using publicly available information obtained from manufacturers’ websites, press releases, and journal articles. Currently available automated sorting equipment and artificial intelligence (AI)-based sorters are evaluated regarding their functionality, efficiency, types of plastics they can sort, throughput, and accuracy. The information compiled captures the progress made during the ten years since similar reports were published. A survey of MRFs, reclaimers, and brokers in the United States identified methods of sorting used for plastic, sorting efficiency, and current practices and challenges encountered at MRFs in sorting plastic recyclables. The commercial sorting equipment can address some of the challenges that MRFs face. However, sorting of film, multilayered, blended, or mixed-material plastics is problematic, as the equipment is typically designed to sort single-component materials. Accordingly, improvements and/or new solutions are considered necessary. Full article

Farmers are often exposed to risk factors for musculoskeletal disorders through lifting, carrying heavy loads, and sustained or repeated full-body bending. Several relevant studies on ergonomic interventions have been conducted for specific agricultural tasks, such as harvesting and pruning, by experts without involving farmers. This study introduces ergonomic interventions to mitigate risk factors in a farming organization that cultivates peaches as the main crop based on ergonomic analysis of the entire peach farming cycle; subjective and objective evaluations of the proposed interventions are also performed. The ergonomic analysis and interventions were established based on consultations provided by an ergonomist, the government, and the organization members. Engineering controls were introduced for powered carts, sorters, and stools to reduce load carrying and awkward postures; moreover, thermal or cooling vests, winter shoes and gloves, and farmer hats were provided to alleviate cold or heat stresses. Administrative controls such as education/training and adjusting work–rest cycles were also recommended after considering the characteristics of the risk factors identified. The scores of the questionnaire survey from the organization members were high (>4.1 out of 5 for five questions), and postural loads for unstable postures by RULA were significantly reduced so as to avoid fast or immediate changes for the postures or working methods assessed. The study results are expected to help promote farmers’ health and enhance farming efficiency. Full article

Most electronic and automotive parts are affixed by bolts. To prevent such bolts from loosening through shock and vibration, anti-loosening coating is applied to their threads. However, during the coating process, various defects can occur. Consequently, as the quality of the anti-loosening coating is critical for the fastening force, bolts are inspected optically and manually. It is difficult, however, to accurately screen coating defects owing to their various shapes and sizes. In this study, we applied deep learning to assess the coating quality of bolts with anti-loosening coating. From the various convolutional neural network (CNN) methods, the VGG16 structure was employed. Furthermore, the gradient-weighted class activation mapping visualization method was used to evaluate the training model; this is because a CNN cannot determine the classification criteria or the defect location, owing to its structure. The results confirmed that external factors influence the classification. We, therefore, applied the region of interest method to classify the bolt thread only, and subsequently, retrained the algorithm. Moreover, to reduce the learning time and improve the model performance, transfer learning and fine tuning were employed. The proposed method for screening coating defects was applied to a screening device equipped with an actual conveyor belt, and the Modbus TCP protocol was used to transmit signals between a programmable logic controller and a personal computer. Using the proposed method, we were able to automatically detect coating defects that were missed by optical sorters. Full article

The development of drug delivery systems for use in the treatment of cardiovascular diseases is an area of great interest. We report herein on an evaluation of the therapeutic potential of a myocardial mitochondria-targeting liposome, a multifunctional envelope-type nano device for targeting pancreatic β cells (β-MEND) that was previously developed in our laboratory. Resveratrol (RES), a natural polyphenol compound that has a cardioprotective effect, was encapsulated in the β-MEND (β-MEND (RES)), and its efficacy was evaluated using rat myocardioblasts (H9c2 cells). The β-MEND (RES) was readily taken up by H9c2 cells, as verified by fluorescence-activated cell sorter data, and was observed to be colocalized with intracellular mitochondria by confocal laser scanning microscopy. Myocardial mitochondrial function was evaluated by a Seahorse XF Analyzer and the results showed that the β-MEND (RES) significantly activated cellular maximal respiratory capacity. In addition, the β-MEND (RES) showed no cellular toxicity for H9c2 cells as evidenced by Premix WST-1 assays. This is the first report of the use of a myocardial mitochondria-targeting liposome encapsulating RES for activating mitochondrial function, which was clearly confirmed based on analyses using a Seahorse XF Analyzer. Full article

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other. Full article

This paper presents a dual-mode low-density parity-check (LDPC) decoding architecture that has excellent error-correcting capability and a high parallelism design for fifth-generation (5G) new-radio (NR) applications. We adopted a high parallelism design using a layered decoding schedule to meet the high throughput requirement of 5G NR systems. Although the increase in parallelism can efficiently enhance the throughput, the hardware implementation required to support high parallelism is a significant hardware burden. To efficiently reduce the hardware burden, we used a grouping search rather than a sorter, which was used in the minimum finder with decoding performance loss. Additionally, we proposed a compensation scheme to improve the decoding performance loss by revising the probabilistic second minimum of a grouping search. The post-layout implementation of the proposed dual-mode LDPC decoder is based on the Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm complementary metal-oxide-semiconductor (CMOS) technology, using a compensation scheme of grouping comparison for 5G communication systems with a working frequency of 294.1 MHz. The decoding throughput achieved was at least 10.86 Gb/s without evaluating early termination, and the decoding power consumption was 313.3 mW. Full article