Search Results (378)

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

Rubber–textile conveyor belts are an important element of large-scale transport systems, which in many cases are subjected to excessive dynamic loads. Assessing the impact resistance of them is essential for ensuring their reliability and longevity. The article focuses on the use of machine learning methods as one of the approaches to the analysis and prediction of the impact resistance of rubber–textile conveyor belts. Based on the data obtained from the design properties of conveyor belts and experimental testing conditions, four models were created (regression model, decision tree regression model, random forest model, ANN model), which are used to analyze and predict the impact force of the force acting on the conveyor belt during material impact. Each model was trained on training data and validated on test data. The performance of each model was evaluated using standard metrics and model indicators. The results of the model analysis show that the most powerful model, ANN, explains up to 99.6% of the data variability. The second-best model is the random forest model and then the regression model. The least suitable choice for predicting the impact force is the regression tree. Full article

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

Based on the low-carbon transition needs of coal-fired boilers, this study conducted industrial trials of direct biomass co-firing on a 620 t/h high-temperature, high-pressure circulating fluidized bed (CFB) boiler, gradually increasing the co-firing ratio. It used compressed biomass pellets, achieving stable 20 wt% (weight percent) operation. By analyzing boiler parameters and post-shutdown samples, the comprehensive impact of biomass co-firing on the boiler system was assessed. The results indicate that biomass pellets were blended with coal at the last conveyor belt section before the furnace, successfully ensuring operational continuity during co-firing. Further, co-firing biomass up rates of to 20 wt% do not significantly impact the fuel combustion efficiency (gaseous and solid phases) or boiler thermal efficiency and also have positive effects in reducing the bottom ash and SO_x and NO_x emissions and lowering the risk of low-temperature corrosion. The biomass co-firing slightly increases the combustion share in the dense phase zone and raises the bed temperature. The strong ash adhesion characteristics of the biomass were observed, which were overcome by increasing the ash blowing frequency. Under 20 wt% co-firing, the annual CO₂ emissions reductions can reach 130,000 tons. This study provides technical references and practical experience for the engineering application of direct biomass co-firing in industrial-scale CFB boilers. Full article

This study analyzes the impact of the type of transported material (overburden, lignite, mixture) on the rate of core damage accumulation in Type St conveyor belts in open-pit mines. The research was conducted using the DiagBelt+ diagnostic system, which enables the assessment of belt core condition without dismantling the belt. Data were collected from over 100 conveyor belt loops, covering segments of varying lengths, ages, and operational histories. Damage density and area were assessed, and differences were analyzed depending on the material type. The results indicate that belt age and damage density vary significantly with material type, while the Resurs indicator (percentage of expected operating time) shows no clear dependence on the material type. A multiple regression analysis was also performed to predict failure density based on operational variables, such as Age, Resurs results, Loop Length, and Segment Length. The regression model explains approximately 46% of the variability in damage density, indicating the need for further research to improve predictive accuracy. The study emphasizes the importance of using non-destructive diagnostic systems to optimize maintenance planning and enhance conveyor belt reliability. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

In order to improve the uniformity of microwave heating, moving components are often added to the cavity. For higher uniformity or greater industrial processing capacity, samples often perform large-scale movements such as rotating and lifting motion or translational motion on a conveyor belt. The microwave heating algorithm based on the ray-casting method (RCM), as proposed in previous studies, can calculate moving complex-shaped samples, but the calculation efficiency is low when the sample moves on a large scale due to the large refined mesh area. To solve this problem, this study introduced a moving mesh combined with the RCM for calculation purposes. A microwave oven model with a rotating and lifting turntable was selected for the analysis. First, the calculation area was divided into a sliding mesh and a telescopic mesh area. The telescopic mesh area was stretched or compressed at different times, which was equivalent to the translational motion of the sample. Then, the electromagnetic parameters were assigned to each mesh point in combination with the boundary recognition algorithm based on the ray-casting method, and the horizontal motion was calculated while calculating the large-scale translation. The proposed method only needs to refine the mesh in the horizontal motion area, which reduces the number of overall meshes. The electromagnetic field distribution obtained by the model during the heating process was verified by the discrete position method. The surface temperature distribution and the real-time curve of the center point temperature were further compared with the RCM. The results show that the average error of the sample center temperature is 2.5% and the calculation time was reduced to 9.8%, which verified the accuracy and efficiency of the proposed method. Finally, the influence of different lifting and rotating speeds on the heating effect was further explored. Full article

With the increasing depth of mining operations, the geological conditions of deep roadways have become increasingly complex. Among these complexities, the issues of fractured zones and groundwater are particularly critical, significantly contributing to the reduced stability of the surrounding rock. This study focuses on the challenging support problem associated with water-bearing fractured surrounding rock in the Y1# belt conveyor roadway of the Wengfu phosphate mine. Through theoretical calculation, laboratory testing, numerical simulation, and field monitoring, the range and displacement of the broken zone in the broken surrounding rock roadway are studied and analyzed. The results show that the physical and mechanical properties of the broken surrounding rock mass are weakened by water, and the range and deformation of the broken zone of the surrounding rock of the water-bearing roadway increase. In response to the failure characteristics of the water-bearing fractured surrounding rock in the Y1# belt conveyor roadway, an optimized support scheme was developed. A combined support system of steel arch frames and localized grouting was proposed to enhance the control of the surrounding rock. Field monitoring data confirmed that the optimized support scheme achieved satisfactory control effectiveness, effectively addressing the stability challenges posed by water-bearing fractured rock masses. Full article

Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging systems on conveyor belts cannot be implemented. Here, we investigate how two fundamentally different handheld infrared spectral devices can identify plastic types by benchmarking the same analysis against a high-resolution bench-top spectral approach. We used the handheld Plastic Scanner, which measures a discrete infrared spectrum using LED illumination at different wavelengths, and the SpectraPod, which has an integrated photonics chip which has varying responsivity in different channels in the near-infrared. We employ machine learning using SVM, XGBoost, Random Forest and Gaussian Naïve Bayes models on a full dataset of plastic samples of PET, HDPE, PVC, LDPE, PP and PS, with samples of varying shape, color and opacity, as measured with three different experimental approaches. The high-resolution spectral approach can obtain an accuracy (mean ± standard deviation) of (0.97 ± 0.01), whereas we obtain (0.93 ± 0.01) for the SpectraPod and (0.70 ± 0.03) for the Plastic Scanner. Differences of reflectance at subsequent wavelengths prove to be the most important features in the plastic-type classification model when using high-resolution spectroscopy, which is not possible with the other two devices. Lower accuracy for the handheld devices is caused by their limitations, as the spectral range of both devices is limited—up to 1600 nm for the SpectraPod, while the Plastic Scanner has limited sensitivity to reflectance at wavelengths of 1100 and 1350 nm, where certain plastic types show characteristic absorbance bands. We suggest that combining selective sensitivity channels (as in the SpectraPod) and illuminating the sample with varying LEDs (as with the Plastic Scanner) could increase the accuracy in plastic-type identification with a handheld device. Full article

The article describes an innovative method for detecting conveyor belt damage using a strain gauge system and deep learning techniques. The strain gauge system records measurement data from conveyor belts. A major challenge encountered during the research was the insufficient amount of measurement data for effectively training deep neural networks. To address this issue, the authors implemented a hybrid data augmentation method that combines generative artificial intelligence techniques and signal analysis. The TimeGAN model, based on Generative Adversarial Networks (GANs), was used to augment data from undamaged belts. Meanwhile, the superposition of one-dimensional observation sequences was applied to generate data representing damages by combining signals from randomly selected undamaged runs with strain gauge system responses to damage, effectively increasing the number of samples while accurately replicating defect conditions. For damage diagnosis, a Long Short-Term Memory Network with an attention mechanism (LSTM-AM) was employed, enabling anomaly detection in strain gauge signals. The application of the LSTM-AM algorithm allows for real-time monitoring of conveyor operation and facilitates precise localization and estimation of damage size through data synchronization. Full article

Industrial Artificial Intelligence (IAI) services are increasingly integral to smart manufacturing, especially in quality assurance tasks like defect detection. This paper presents the design, implementation, and evaluation of a video-based visual anomaly detection (VAD) system that runs at inspection stations on a smart shop floor. Our system processes real-time video streams from multiple cameras mounted around a conveyor belt to detect surface-level defects in mechanical components. To meet stringent latency and accuracy requirements, an edge-cloud architecture powered by AI accelerators and InfiniBand networking is adopted. The IAI service features key frame extraction modules, fine-tuned lightweight VAD models, and optimization techniques such as batching and microservice-level parallelism. The design choices of AI modules are carefully evaluated to balance effectiveness and efficiency. As a result, the system latency is optimized by 57%. In addition to the high-performance solution, a cost-efficient alternative is also suggested that is able to complete the task within the time frame. Full article

Belt conveyors are essential systems for the continuous transport of various materials across many industries, particularly in bulk material handling for mining. While they are often the most economical solution, they still consume a significant amount of energy. This article discusses the latest advancements and the current state of energy-saving solutions for belt conveyors. Key solutions include low-friction belts, variable-frequency drives (VFDs), monitoring systems, automation, and regenerative belt conveyors. It is important to note that selecting the right construction parameters, performing preventive maintenance, and using appropriate materials are crucial for reducing energy consumption. By combining these energy-saving technologies with well-chosen construction parameters, it is possible to develop sustainable belt conveyors. Full article

Rubber is widely used in daily lives, such as in automobile tires, conveyor belts, sealing rings, and gaskets. The performance of rubber determines its service life. Therefore, it is of crucial importance to improve the performance of rubber. Theoretical studies have found that the inherent properties of nanofillers themselves, the interfacial bonding force between fillers and the matrix, and the uniform dispersibility of nanofillers in the polymer matrix are the most significant factors for enhancing the performance of rubber nanocomposites. This study systematically investigated the synergistic enhancement effect of silicon carbide (SiC) and multi-walled carbon nanotubes (MWCNTs) on the mechanical and thermal properties of ethylene–butene–terpolymer (EBT) composites. By optimizing the addition amount of fillers and improving the interfacial bonding between fillers and the matrix, the influence of filler content on the properties of composites was studied. The results demonstrate that the addition of SiC and MWCNTs significantly improved the storage modulus, tensile strength, hardness, and thermal stability of the composites. In terms of mechanical properties, the tensile strength of the composites increased from 6.68 MPa of pure EBT to 8.46 MPa, and the 100% modulus increased from 2.14 MPa to 3.81 MPa. Moreover, hardness was significantly enhanced under the reinforcement of SiC/CNT fillers. In terms of thermal stability, the composites exhibited excellent resistance to deformation at high temperatures. Through the analysis of the mechanical and thermal properties of the composites, the synergistic enhancement mechanism between SiC and MWCNTs was revealed. The research results provide a theoretical basis for the design and engineering applications of high-performance ethylene–butylene rubber composites. Full article

Rising raw material costs and complex global supply chains have reduced the durability and availability of conveyor belts. In response, condition-based maintenance (CBM) with in situ diagnostics has become essential. This case study from a Polish lignite mine shows how subjective visual inspections were replaced with objective, repeatable measurements of belt core condition and thickness. Shifting refurbishment decisions from the plant to the conveyor improved success rates from 70% to over 90% and optimized belt lifecycle management. Sensor-based monitoring enables predictive maintenance, reduces premature or delayed replacements, increases belt reuse, lowers costs, and supports the circular economy by extending belt core life and reducing raw material demand. The study demonstrates how real-time, sensor-based diagnostics using inductive and ultrasonic technologies supports predictive maintenance of conveyor belts, improving refurbishment efficiency and lifecycle management. Full article

In this work, we describe the mechanical design, analysis, and tests of an innovative automatic high-speed industrial postal canceling machine. The main novelties of this machine are automatic feeding of heterogeneous letters in a very compact design, high speed of letter processing (>22,000 letters per hour), and letter jamming detection. This is the first time that such an industrial and automatic machine is being constructed in a reduced desktop size and is able to handle heterogeneous types of letters at a high-speed rate. The cancel machine includes a linear feeding belt for the letter feeding, assisted by a vacuum letter gripper and a set of conveyor belts that guide the letter to the impression. The machine has low energy consumption and is easy to maintain with multiple safety limit switches. The design is fully compliant with the new European machine regulations. We show the details of the mechanical design, and we present several performance and speed tests, demonstrating that the machine achieves more than 99.8% of canceling rate with different sizes and thicknesses of letters, padded and mixed-size, while having a letter canceling speed of more than 22,000 letters per hour. Full article