Search Results (29)

Innovations in manual waste sorting have stagnated for decades, despite the increasing global demand for efficient recycling solutions. The recAIcle system introduces an innovative AI-powered assistance system designed to modernise manual waste sorting processes. By integrating machine learning, continual learning, and projection-based augmentation, the system supports sorting workers by highlighting relevant waste objects on the conveyor belt in real time. The system learns from the decision-making patterns of experienced sorting workers, enabling it to adapt to operational realities and improve classification accuracy over time. Various hardware and software configurations were tested with and without active tracking and continual learning capabilities to ensure scalability and adaptability. The system was validated in initial trials, demonstrating its ability to detect and classify waste objects and providing augmented support for sorting workers with high precision under realistic recycling conditions. A survey complemented the trials and assessed industry interest in AI-based assistance systems. Survey results indicated that 82% of participating companies expressed interest in supporting their staff in manual sorting by using AI-based technologies. The recAIcle system represents a significant step toward digitising manual waste sorting, offering a scalable and sustainable solution for the recycling industry. Full article

Pipe conveyors provide an environmentally friendly alternative to open-troughed bulk solids conveyance, particularly for long or complex routing applications. However, the sustainability of this technology is compromised by unstable operations. Complex routing, operational variations, and environmental factors create uneven contact forces, triggering belt rotation. This is a critical failure mode that requires continuous monitoring throughout the conveyor’s lifecycle. Insufficient failure data represents a typical challenge for this application. This study hypothesized technological principles that constitute the minimum requirements for enabling the scaling of industrial applications of belt rotation monitoring. Enabling technologies were adopted to foster innovation, and a physical prototype was implemented to address data scarcity for this failure mode. Using a controller-responder wireless network of ESP32 Industrial Internet of Things devices, we developed a belt-independent measurement system with multiparameter capability. Key criteria for detecting unsafe operational states and a criticality-based approach for determining optimal measuring unit quantities were established. The measurement results demonstrated suitable precision for digitization objectives: overlap angle (3.3107° ± 16.7562°), pipe diameter (+13.3850 ± 7.2114 mm), and overlap length (−26.2750 ± 25.1536 mm), based on 307 samples with a latency of 350.1303 ms. The framework demonstrates potential for industrial deployment with acceptable performance for real-time monitoring. Full article

Industrial electric drives account for a dominant share of electricity consumption in manufacturing, making their optimal configuration a critical factor for both sustainability and cost reduction. Traditional design approaches based on prototyping and empirical testing are often costly and insufficient for systematically exploring alternative configurations. This study introduces an integrated computational framework that combines digital twin (DT) modeling and virtual commissioning (VC) to enable energy-aware configuration of industrial electric drive systems at early design stages. The methodology employs parameterized component models derived from manufacturer catalog data, implemented in a commercial simulation environment and integrated into an industrial-grade VC platform. Validation is performed on two conveyor-based testbeds, enabling systematic comparison of simulation outputs with physical measurements. The results demonstrate predictive accuracy sufficient to quantify trade-offs in energy consumption, losses, and efficiency across different vendor solutions. Case studies involving belt and strap conveyors highlighted how the framework supports vendor-neutral decision making, revealing nonintuitive optimization trade-offs between minimizing energy consumption and maximizing efficiency. The proposed framework advances sustainable automation by embedding energy analysis directly into commissioning workflows, offering reproducible, scalable, and cross-domain applicability. Its modular design supports transfer to sectors such as renewable energy, transportation, and biomedical mechatronics, where energy efficiency is equally decisive. Full article

Coriander is a significant crop, playing an essential role in daily life for various purposes, including flavouring curries and medicinal uses, among others. Despite its importance, coriander is still harvested manually. To address this, developed a self-propelled battery-operated coriander harvester, designed with ergonomics, environmental sustainability and affordability for small and marginal farmers in mind. The harvester is equipped with a main frame, a lead-acid battery, a BLDC motor, a reciprocating cutter bar, a PU conveyor belt, a collection bag, a handle, and transport wheels. The harvester was tested on the coriander crop, and the results were analyzed using Design Expert software to optimize various operational parameters. The harvester’s performance was evaluated at three forward speeds: 1.5 km/h, 2 km/h, and 2.5 km/h, resulting in covered areas of 0.114 ha, 0.164 ha, and 0.22 ha, with field efficiency values of 76%, 82%, and 88%, respectively. Optimal harvesting conditions were identified by design expert software at a forward speed of 1.64 km/h, with a conveyor driving pulley at level 3 (50.8 mm) and a cutting height at level 2 (75 mm). Under these conditions, the harvester achieved a harvesting efficiency of 97.24% and a cutting efficiency of 98.2%, with minimal conveying loss of 0.96%. The theoretical field capacity was 0.16 ha/h, the actual field capacity was 0.131 ha/h, and the overall field efficiency was 81.8%. 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

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