Search Results (91)

The research presented in this article aimed to analyze the impact of circular economy initiatives on the European Union environmental goods and services sector. Data from the Eurostat databases were used to conduct the research. Validation of the five research hypotheses involved the formulation of two regression equations: the first one focused on innovation and recycling performance indicators, and the second equation focused on circular-economy indicators. Both models were estimated using country fixed-effects regression with Driscoll–Kraay standard errors to ensure robust inference. The regression model was selected based on the Hausman test and F-test, but tests for autocorrelation, heteroscedasticity, and multicollinearity were also performed. The main findings, as the results suggest, refer to the central role of private investment in circular sectors and resource productivity, both exerting positive and significant effects on the environmental goods and services sector (EGSS). The material footprint also shows a positive effect, but in contrast, the circular material use rate does not display a significant impact, indicating that increases in the share of recycled materials do not automatically translate into greater economic value. Full article

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the state of health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge–discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives. Full article

The recycling and remanufacturing of end-of-life (EoL) electric vehicle (EV) batteries are urgent challenges for a circular economy. Disassembly is crucial for handling EoL EV batteries due to their inherent uncertainties and instability. The human–robot collaborative disassembly of EV batteries as a semi-automated approach has been investigated and implemented to increase flexibility and productivity. Unscrewing is one of the primary operations in EV battery disassembly. This paper presents a new method for the robotic unfastening and collecting of screws, increasing disassembly efficiency and freeing human operators from dangerous, tedious, and repetitive work. The design inspiration for this method originated from how human operators unfasten and grasp screws when disassembling objects with an electric tool, along with the fusion of multimodal perception, such as vision and touch. A robotic disassembly system for screws is introduced, which involves a collaborative robot, an electric spindle, a screw collection device, a 3D camera, a six-axis force/torque sensor, and other components. The process of robotic unfastening and collecting screws is proposed by using position and force control. Experiments were carried out to validate the proposed method. The results demonstrate that the screws in EV batteries can be automatically identified, located, unfastened, and removed, indicating potential for the proposed method in the disassembly of EoL EV batteries. Full article

Circular economy is a crucial strategy for achieving sustainable development. The use of solar PV, which is a renewable energy source, has been considered a popular indicator to measure and evaluate the circularity of an economy and enterprises. The recycling of solar PV panels optimises resource use and reduces the need for virgin materials. However, it does not automatically indicate an environmental advantage if the recovering and recycling processes are energy- or emission-intensive. The paper applies life cycle assessment to quantify the material demand for the Italian solar PV sector and contributions of solar PV end-of-life strategies to the enhancement of the circular economy. It is identified that the material intensity of the Italian solar PV sector increases from 4.67 g Sb eq to 5.20 g Sb eq per MWh by 2040 due to the change in technology mix. At the same time, the total material demand, as well as demand for specific materials, increases over the years, from 2008 to 2040. The strategy on recovery, recycling and reintegration of materials slightly reduces the material demand, from 816 tonnes Sb eq to 814 tonnes Sb eq in 2040. It also brings the benefits of reducing all the life cycle impacts, such as greenhouse gas emissions, energy demand, etc. Full article

Recycling has been acknowledged as a viable alternative for the management of plastic refuse. An automatic sorting system is required by the industry to predict the plastic waste based on the type before it is recycled. The plastic sorting system automation requires intelligent computing as a software system that can predict the type of plastic accurately. The ensemble method is a method that combines several single prediction methods based on machine learning into an algorithm to obtain better performance. This study aims to build intelligent computing for the automation of digital image-based plastic waste sorting systems using an ensemble method built from three naïve Bayes single prediction methods. The three single models consist of one Naïve Bayes (NB) model with crisp discretization and two NB models with fuzzy discretization, namely those using a combination of linear–triangular fuzzy membership functions and a combination of linear–trapezoidal fuzzy membership functions. We hypothesize that the performance of each single model and the proposed ensemble model is different, and the performance of the ensemble model is higher than all the single models used to build it. The hypothesis is proven, and there is an increase in performance from each single method to the ensemble method ranging from 2.06% to 5.56%. The evidence of this hypothesis also shows that the performance of the proposed prediction model using the ensemble method built from three naive Bayes models is high and robust. Full article

The residential separate collection of waste is the first stage in waste recyclability for sustainable development. The paper focuses on designing and implementing a low-cost residential automatic waste sorting bin (RBin) for recycling, alleviating the user’s classification burden. Next, an analysis of two object identification and classification models was conducted to sort materials into the categories of cardboard, glass, plastic, and metal. A major challenge in sorting classification is distinguishing between glass and plastic due to their similar visual characteristics. The research assesses the performance of the Azure Custom Vision Service (ACVS) model, which achieves high accuracy on training data but underperforms in real-time applications, with an accuracy of 95.13%. In contrast, the second model, the Custom Waste Sorting Model (CWSM), demonstrates high accuracy (96.25%) during training and proves to be effective in real-time applications. The CWSM uses a two-tier approach, first identifying the object descriptively using the Google Vision API Service (GVAS) model, followed by classification through the CWSM, a predicate-based custom model. The CWSM employs the LbfgsMaximumEntropyMulti algorithm and a dataset of 1000 records for training, divided equally across the categories. This study proposes an innovative evaluation metric, the Weighted Classification Confidence Score (WCCS). The results show that the CWSM outperforms ACVS in real-world testing, achieving a real accuracy of 99.75% after applying the WCCS. The paper explores the importance of customized models over pre-implemented services when the model uses characteristics and not pixel-by-pixel examination. Full article

In Japan, local governments implore residents to remove the batteries from small-sized electronics before recycling them, but some products still contain lithium-ion batteries. These residual batteries may cause fires, resulting in serious injuries or property damage. Explosive materials such as mobile batteries (such as power banks) have been identified in fire investigations. Therefore, these fire-causing items should be detected and separated regardless of whether small-sized electronics recycling or other recycling processes are in use. This study focuses on the automatic detection of fire-causing items using deep learning in recycling small-sized electronic products. Mobile batteries were chosen as the first target of this approach. In this study, MATLAB R2024b was applied to construct the You Only Look Once version 4 deep learning algorithm. The model was trained to enable the detection of mobile batteries. The results show that the model’s average precision value reached 0.996. Then, the target was expanded to three categories of fire-causing items, including mobile batteries, heated tobacco (electronic cigarettes), and smartphones. Furthermore, real-time object detection on videos using the trained detector was carried out. The trained detector was able to detect all the target products accurately. In conclusion, deep learning technologies show significant promise as a method for safe and high-quality recycling. Full article

Recycled glass has been the focus of attention owing to its role in reducing plastic waste and further increasing the demand for glass containers. Cosmetics glass bottles require strict quality inspections because of the frequent handling, safety concerns, and other factors. During manufacturing, glass bottles sometimes develop chips on the top surface, rim, or screw threads of the bottle mouth. Conventionally, these chips are visually inspected by inspectors; however, this process is time consuming and prone to inaccuracies. To address these issues, automatic inspection using image processing has been explored. Existing methods, such as dynamic luminance value correction and ring-shaped inspection gates, have limitations: the former relies on visible light, which is strongly affected by natural light, and the latter acquires images directly from above, resulting in low accuracy in detecting chips on the lower part of screw threads. To overcome these challenges, this study proposes a method that combines infrared backlighting and image processing to determine the range of screw threads and detect chips accurately. Experiments were conducted in an experimental environment replicating an actual factory production line. The results confirmed that the detection accuracy of chipping was 99.6% for both good and defective bottles. This approach reduces equipment complexity compared to conventional methods while maintaining high inspection accuracy, contributing to the productivity and quality control of glass bottle manufacturing. Full article

Despite the significant recycling potential, a massive generation of plastic waste is observed year after year. One of the causes of this phenomenon is the issue of ineffective waste stream sorting, primarily arising from the uncertainty in the composition of the waste stream. The recycling process cannot be carried out without the proper separation of different types of plastics from the waste stream. Current solutions in the field of automated waste stream identification rely on small-scale datasets that insufficiently reflect real-world conditions. For this reason, the article proposes a real-time identification model based on a CNN (convolutional neural network) and a newly constructed, self-built dataset. The model was evaluated in two stages. The first stage was based on the separated validation dataset, and the second was based on the developed test bench, a replica of the real system. The model was evaluated under laboratory conditions, with a strong emphasis on maximally reflecting real-world conditions. Once included in the sensor fusion, the proposed approach will provide full information on the characteristics of the waste stream, which will ultimately enable the efficient separation of plastic from the mixed stream. Improving this process will significantly support the United Nations’ 2030 Agenda for Sustainable Development. Full article

The productivity of the construction industry is about half that of the manufacturing industry, and the labor shortage in the construction industry is serious; therefore, improving productivity using information and communication technology (ICT) is an urgent issue. In addition, in civil engineering works, the number of projects that handle multiple types of soil and sand is increasing due to the recycling of construction waste soil; thus, traceability management is important to ensure quality. This paper presents a system that uses sensing on soil-transporting dump trucks and ICT to record which soil was piled up where with the aim of improving the efficiency of traceability management in earthwork construction. This system automatically creates traceability data by linking sensing data and data from the compaction management system via an application. This eliminates the need to record and manage the earthwork location, which was previously required manually to create traceability data, and reduces the labor and manpower required for traceability management. The created traceability data are automatically assigned attribute information such as the construction date and soil information; consequently, they can be used to check the construction history in the future. Full article

To achieve the required recycling rates, organic recycling via composting should be widely introduced in Poland for selectively collected biowaste. However, this process not only produces compost but also leachate (L_CB), a nitrogen- and organics-rich liquid by-product. So far there has been limited information on the application of anaerobic digestion (AD) for treating L_CB, which has fermentative potential. However, for effective methane production (MP) via AD, the ratio of chemical oxygen demand to total Kjeldahl nitrogen (COD/TKN) and pH of L_CB are too low; thus, it should be co-digested with other organics-rich waste, e.g., glycerine (G). The present study tested the effect of G content in feedstock (in the range of 3–5% (v/v)) on the effectiveness of co-digestion with L_CB, based on MP and the removal of COD. MP was accessed by using an automatic methane potential test system (AMPTS). Regardless of the feedstock composition (L_CB, or L_CB with G), the efficiency of COD removal was over 91%. Co-digestion not only increased MP by 6–15%, but also the methane content in the biogas by 4–14% compared to L_CB only (353 NL/kg COD_added, 55%). MP and COD removal proceeded in two phases. During co-digestion in the 1st phase, volatile fatty acids (VFA) accumulated up to 2800 mg/L and the pH decreased below 6.8. The presence of G altered the shares of individual VFA and promoted the accumulation of propionic acid in contrast to L_CB only, where caproic acid predominated. An initial accumulation of propionic acid and acidification in the mixtures decreased the kinetic constants of MP (from 0.79 to 0.54 d⁻¹) and the rate of COD removal (from 2193 to 1603 mg/(L·d)). In the 2nd phase, the pH recovered, VFA concentrations decreased, and MP was no longer limited by these factors. However, it should be noted that excessive amounts of G, especially in reactors with constant feeding, may cause VFA accumulation to a greater extent and create a toxic environment for methanogens, inhibiting biogas production. In contrast, digestion of L_CB only may lead to ammonium buildup if the COD/TKN ratio of the feedstock is too low. Despite these limitations, the use of AD in the treatment of L_CB as a sustainable “closed-loop nutrient” technology closes the loop in composting of biowaste. Full article

This study investigates the feasibility of using friction stir extrusion (FSE) recycled aluminum wires as filler metals for gas tungsten arc welding (GTAW) and additive manufacturing applications. A NC-controlled GTAW feeding system was developed to enable the deposition of these recycled wires. The effect of cleaning the machining chips before the FSE process on the quality of the manufactured wires and the resulting welded beads was evaluated. Wires produced from uncleaned chips and cleaned chips were compared in terms of their external appearance, ductility, and the presence of porosity after the weld deposition. The results showed that cleaning the chips before the FSE process is crucial for obtaining more uniform wires with better ductility. Automatic GTAW deposition using cleaned wires resulted in significantly improved bead geometry, reduced external porosity, and overall better quality compared to uncleaned wires. However, both wire types exhibited internal porosity, with uncleaned wires showing the worst performance. The findings demonstrate the potential of using FSE recycled aluminum wires for welding and additive manufacturing while highlighting the importance of chip cleaning and the need for further optimization to minimize porosity in the deposited material. Full article

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs). The surface morphology of the fibres was characterised using Scanning Electron Microscopy (SEM), and their structural integrity was assessed through Thermogravimetric Analysis (TGA) and Raman spectroscopy. An automatic analysis method based on optical microscopy images was also developed to quantify filament loss during the recycling process. Mechanical testing of single fibres and yarns showed that although rCFs from both recycling methods exhibited a ~20% reduction in tensile strength compared to reference fibres, the application of sizing significantly mitigated these effects (~10% reduction). X-ray Photoelectron Spectroscopy (XPS) further confirmed the introduction of functional oxygen-containing groups on the fibre surface, which improved fibre-matrix adhesion. Overall, the results demonstrate that plasma-enhanced solvolysis was more effective at fully decomposing the resin, while the subsequent application of sizing enhanced the mechanical performance of rCFs, restoring their properties closer to those of virgin fibres. Full article

Sorting is an important construction waste management tool to increase recycling rates and reduce pollution. Previous studies have used robots to improve the efficiency of construction waste recycling. However, in large construction sites, it is difficult for a single robot to accomplish the task quickly, and multiple robots working together are a better option. Most construction waste recycling robotic systems are developed based on a client-server framework, which means that all robots need to be continuously connected to their respective cloud servers. Such systems are low in robustness in complex environments and waste a lot of computational resources. Therefore, in this paper, we propose a pixel-level automatic construction waste recognition platform with high robustness and low computational resource requirements by combining multiple computer vision technologies with edge computing and cloud computing platforms. Experiments show that the computing platform proposed in this study can achieve a recognition speed of 23.3 fps and a recognition accuracy of 90.81% at the edge computing platform without the help of network and cloud servers. This is 23 times faster than the algorithm used in previous research. Meanwhile, the computing platform proposed in this study achieves 93.2% instance segmentation accuracy on the cloud server side. Notably, this system allows multiple robots to operate simultaneously at the same construction site using only a single server without compromising efficiency, which significantly reduces costs and promotes the adoption of automated construction waste recycling robots. Full article

Plastic pollution is a global problem affecting the environment and, consequently, people’s well-being. Careful and timely end-of-life plastic recycling is certainly a way, albeit a partial one, to remedy the problem. The immediate identification and selection of the different types of plastic materials in the recycling process certainly facilitate its recovery and reuse, allowing the damage caused by plastic emission into the environment to be limited. Recently, new technologies for automatic sorting of plastics based upon fluorescent tagging have been considered. This article reports the synthesis and characterization of fluorescent copolymers of poly(methyl methacrylate) (PMMA) that could be potentially used as fluorescent markers of commercial PMMA. Poly(methylmetacrylate-co-2-(9-carbazolyl)ethyl methacrylate) (P(MMA-co-CEMA)) and poly(methylmetacrylate-co-7-methacryloyloxycoumarin) (P(MMA-co-MAOC)) samples containing a small number of fluorescent units (<4%) were synthesized by free-radical polymerization. All copolymer samples show chemico-physical properties like those of pure PMMA and produce fluorescence emission under 290 nm wavelength excitation. P(MMA-co-CEMA)s and P(MMA-co-MAOC)s were also tested as fluorescent dyes for PMMA identification. The experimental results demonstrate that PMMA/P(MMA-co-CEMA) and PMMA/P(MMA-co-MAOC) blends prepared using 1% by weight of fluorescent copolymer show a homogeneous morphology completely similar to pure PMMA and are still optically active. Full article