Search Results (25)

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

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperform in noisy or changing environments due to heavy reliance on manual feature engineering and rule-based systems. In response, advanced machine learning, deep learning, and sophisticated signal processing techniques have emerged as transformative solutions for fault detection and predictive maintenance. To address the complexity of these advancements and their practical implications, this review combines analyses from large language models with expert validation to categorize key methodologies—spanning classical machine learning models, deep neural networks, and hybrid physics–data approaches. It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. By synthesizing these insights, this article offers a path forward for the adoption of new technologies (deep learning, IoT/Industry 4.0, etc.) in complex industrial contexts, anticipating the collaborative and sustainable paradigms of Industry 5.0, where human–machine collaboration and sustainability play central roles. Full article

This review explores the evolving landscape of sustainable textile manufacturing, with a focus on rubber-based materials for various industrial applications. The textile and rubber industries are shifting towards eco-friendly practices, driven by environmental concerns and the need to reduce carbon footprints. The integration of sustainable textiles in rubber-based products, such as tires, conveyor belts, and defense products, is becoming increasingly prominent. This review discusses the adoption of natural fibers like flax, jute, and hemp, which offer biodegradability and improved mechanical properties. Additionally, it highlights sustainable elastomer sources, including natural rubber from Hevea brasiliensis and alternative plants like Guayule and Russian dandelion, as well as bio-based synthetic rubbers derived from terpenes and biomass. The review also covers sustainable additives, such as silica fillers, nanoclay, and bio-based plasticizers, which enhance performance while reducing environmental impact. Textile–rubber composites offer a cost-effective alternative to traditional fiber-reinforced polymers when high flexibility and impact resistance are needed. Rubber matrices enhance fatigue life under cyclic loading, and sustainable textiles like jute can reduce environmental impact. The manufacturing process involves rubber preparation, composite assembly, consolidation/curing, and post-processing, with precise control over temperature and pressure during curing being critical. These composites are versatile and robust, finding applications in tires, conveyor belts, insulation, and more. The review also highlights the advantages of textile–rubber composites, innovative recycling and upcycling initiatives, addressing current challenges and outlining future perspectives for achieving a circular economy in the textile and rubber sectors. Full article

The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failure risks. Traditional maintenance strategies often fail to prevent unexpected downtimes, safety hazards, and economic losses. As a response, industries are integrating predictive monitoring technologies, including machine learning, the Internet of Things, and digital twins, to enhance early fault detection and optimize maintenance strategies. This Systematic Literature Review analyzes 166 high-impact studies from Scopus and Web of Science, identifying key trends in fault detection algorithms, hybrid AI models, and real-time monitoring techniques. The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. Despite these advancements, challenges such as data standardization, model scalability, and system interoperability persist, requiring further research. Future work should focus on real-time AI applications, explainable models, and academia-industry collaboration to accelerate the implementation of intelligent maintenance solutions, ensuring greater reliability, efficiency, and sustainability in mining operations. Full article

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP). Full article

In this paper, machine vision technology is used to recognize the coal flow on a conveyor belt and control the running speed of a motor according to the coal flow on the conveyor belt to achieve an energy-saving effect and provide technical support for the sustainable development of energy. In order to improve the accuracy of coal flow recognition, this paper proposes the color gain-enhanced multi-scale retina algorithm (AMSRCR) for image preprocessing. Based on the YOLOv8s-cls improved deep learning algorithm YOLO-CFS, the C2f-FasterNet module is designed to realize a lightweight network structure, and the three-dimensional weighted attention module, SimAm, is added to further improve the accuracy of the network without introducing additional parameters. The experimental results show that the recognition accuracy of the improved algorithm YOLO-CFS reaches 93.1%, which is 4.8% higher, and the detection frame rate reaches 32.68 frame/s, which is 5.9% higher. The number of parameters is reduced by 28.4%, and the number of floating-point operations is reduced by 33.3%. These data show that the YOLO-CFS algorithm has significantly improved the accuracy, lightness, and reasoning speed in the coal mine environment. Furthermore, it can satisfy the requirements of coal flow recognition, realize the energy-saving control of coal mine conveyor belts, and achieve the purpose of sustainable development of the coal mining industry. Full article

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management. Full article

This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt’s destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal. Full article

This work presents the multi-variant robotization of the process of sealing car radiators. Three design solutions have been proposed for the tank sealing station, in which the seal is applied on a stationary worktable, on a rotary positioner and on a belt conveyor. These solutions were compared in terms of process time, but also energy consumption. The energy optimization of robotic processes is one of the elements of effective production. First, a review of the use of industrial robots in assembly processes is provided and the structure of car radiators is presented. Next, the basic technological process of producing a car radiator is described, especially the process of applying a liquid gasket. Then, the designed robotic stations and conclusions from the simulations are presented, along with the selection of the most sustainable variant of the robotic station. The results of the simulations are useful in reducing the robot’s operating time and energy consumption while maintaining the appropriate process quality. Full article

Communities and authorities have been dismayed by globally recorded tailings storage facility (TSF) failures in recent years, which have negatively affected the safety of people and the integrity of the environment. In this context, obtaining the social and environmental license to operate TSFs has become a challenging process for mining companies. This has promoted the trend of using mine tailings dewatering technologies in the mining industry, with dry stacking of filtered mine tailings being recognized worldwide as one of the most acceptable, safe, and environmentally friendly solutions. This article presents a new paradigm in managing mine tailings, with disruptive and futuristic characteristics, considering the dry stacking of filtered mine tailings for large-scale industrial production rates over 100,000 metric tons per day (mtpd). Aspects of filtered tailings management are discussed, such as (i) dewatering process plant with thickening/filtering equipment, (ii) conveyance using fixed and movable conveyor belts, (iii) construction of dry stacking of filtered mine tailings facility, and (iv) implementation of Industry 4.0 technologies for automation of the mining processes. Finally, the article discusses how the large-scale filtered mine tailings solution is applied, considering the advances in the equipment’s performance and implementation of Industry 4.0 technologies as well as the experience gained worldwide in several mining operations. The future global trend is that mining operations with high daily production of mine tailings will apply dry stacking technology without dams to guarantee sustainability, promote continuity of the mining business, ensure the safety of communities, and conserve the environment. Full article

Contemporary three-dimensional physics-based simulations of the solar convection zone disagree with observations. They feature differential rotation substantially different from the true rotation inferred by solar helioseismology and exhibit a conveyor belt of convective “Busse” columns not found in observations. To help unravel this so-called “convection conundrum”, we use a three-dimensional pseudospectral simulation code to investigate how radially non-uniform viscosity and entropy diffusivity affect differential rotation and convective flow patterns in density-stratified rotating spherical fluid shells. We find that radial non-uniformity in fluid properties enhances polar convection, which, in turn, induces non-negligible lateral entropy gradients that lead to large deviations from differential rotation geostrophy due to thermal wind balance. We report simulations wherein this mechanism maintains differential rotation patterns very similar to the true solar profile outside the tangent cylinder, although discrepancies remain at high latitudes. This is significant because differential rotation plays a key role in sustaining solar-like cyclic dipolar dynamos. Full article

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes. Full article

Pipe conveyors provide sustainable solutions for environmentally sensitive or topographically complex powdered and bulk-solid handling processes; however, belt rotation is among the most critical failure modes of these equipment, influencing engineering, operational, and maintenance activities throughout the conveyors’ lifecycles. Position changes in the overlap are mechanical responses to uneven contact forces between the vulcanizing rubber belt and the idler rolls, owing to the highly nonlinear process of the belt folding from a trough to a tubular shape, and no method for quantifying the belt’s stability is currently available. In this study, we analyzed the failure mode of belt rotation and proposed a linearized model of an overlap stability index to evaluate the resilience of the overlap position through a case study of a short-flight curved pipe conveyor. Our proposal considers an interference model between the simulated torque of a curved flight in a pipe conveyor and the calculated torque of its equivalent straight flight by using kernel-smoothed density functions. It is adapted to incorporate adjustment factors for the filling degree based on simulations, the effect of the overlap in the forming force of the belt, the remaining useful life of the belt, and the coefficients of friction between the belt back cover and the idler rolls due to adhesion and hysteresis. An application was developed to calculate the belt’s rotational holding torque and rotary moment by processing real operational data, simulated contact forces, and the relevant equipment parameters. This analysis identified the reduced transverse bending stiffness and increased belt tension forces as the root causes for position changes with a loss of contact in the upper idler rolls of curved flights 10, 13, 15–16, and 17. The contributing factors included spots of augmented contact forces during the initial stages of the belt lifespan in curved flights 15–16, which presented unstable conditions due to increased opening forces, with an OSI of 0.8657. Furthermore, we proposed corrective and preventive action plans, an optimized replacement interval for the belt, and recommendations for design changes according to the relevant standards. Full article

This article aims to enhance technological advancements in the classification of polyethylene terephthalate (PET) bottle plastic, positively impacting sustainable development and providing effective solutions for collection centers (CC) in Mexico. Three experimental designs and machine learning tools for data processing were developed. The experiments considered three factors: bottle size, liquid volume, and bottle labels. The first experiment focused on determining the sensor distance from post-consumer PET bottles. The second experiment aimed to evaluate the sensor’s detection ability with varying liquid levels, while the third experiment assessed its detection capability for bottle labels. A digital lux meter integrated with a microcontroller was developed to monitor illuminance in post-consumer PET bottles containing liquid as they moved through a conveyor belt at an average rate of three bottles per second. The implemented methodology successfully detected liquids inside transparent PET bottles when they contained beverages ranging from 25% to 100% of their capacity. This study highlights the feasibility of implementing an affordable design for identifying bottles with liquids at CC. Full article