Search Results (284)

Waste printed circuit boards (WPCBs) account for approximately 3%–6% of electrical and electronic equipment (WEEE) and contain high concentrations of valuable metals such as copper, often at levels higher than those in natural ores. Consequently, WPCB recycling represents an important opportunity for resource recovery through urban mining and supports the transition toward a circular economy. This study investigates the application of a Falcon centrifugal concentrator for the gravity separation of metallic and non-metallic fractions from WPCBs, with a focus on fine particles below 300 μm. Despite its potential, this method has received little attention, particularly in research. Optimal operating conditions were identified as 80 Hz rotation frequency and 1 LPM water flow rate for particles −100 μm, and 30 Hz rotation frequency and 3 LPM water flow rate for particles in the −300 + 100 μm range. Under these conditions, copper recovery reached 98.25% with Cu content of 10.34% for the coarse fraction and 95.97% with Cu content of 4.47% for the fine fraction after a cleaner stage. The results demonstrate that Falcon gravity concentration is an efficient technique for recovering fine metallic particles and outperforms the multi-gravity separator (MGS). A sustainable beneficiation flowsheet is proposed to enhance metal recovery and reduce environmental impacts. Full article

The rapid accumulation of Waste Electrical and Electronic Equipment (WEEE) presents severe environmental and resource challenges in high-density metropolises. Traditional reverse logistics (RL) network designs often overlook urban morphological constraints and treat recovery rates as static parameters. To address these gaps, this study proposes a GIS-integrated low-carbon WEEE RL framework. A Spatial Multi-Criteria Decision Analysis (MCDA) workflow first deduces optimal facility layouts avoiding ecological exclusion zones. Subsequently, a Fuzzy Mixed-Integer Linear Programming (FMILP) model endogenizes the dynamic recovery rate and enforces discrete vehicle dispatching, solved via an advanced Geospatially Constrained Multiple-Priority Genetic Algorithm (MPGA). Validated in Jinan, China, the framework consistently outperforms contemporary benchmarks. Crucially, it reveals that traditional continuous models underestimate urban carbon footprints by 34.6%. By adopting the optimal spatial compromise, policymakers can achieve a 19.9% carbon reduction at a marginal 12.7% profit sacrifice, effectively harmonizing decarbonization with commercial viability. Full article

The expansion of climate monitoring networks has generated an increasing accumulation of end-of-life meteorological sensors, creating a specialized stream of waste electrical and electronic equipment (WEEE) that remains largely unaddressed in developing countries. This study presents a material characterization and sustainable management framework for obsolete meteorological sensors installed in automatic weather stations in Ecuador. A hybrid methodological approach was applied, combining field inventory of 16 stations, gravimetric measurements, and analysis of manufacturer technical specifications to estimate material composition and recovery potential. Results show that 65–90% of the total sensor mass consists of recyclable materials, including aluminum, stainless steel, copper, glass, and engineering polymers. A smaller fraction contains components requiring controlled management due to the potential presence of hazardous additives, such as PVC (polyvinyl chloride) elements and electronic microdevices. Based on these findings, a multi-phase management protocol is proposed, incorporating selective disassembly, material segregation, traceability mechanisms, and processing under extended producer responsibility principles. The framework supports circular economy strategies and offers a replicable model for improving sustainability in climate monitoring infrastructure and specialized WEEE management in low- and middle-income countries. Full article

The mechanical recycling of styrenic polymers from waste electrical and electronic equipment (WEEE) is often limited by thermomechanical degradation occurring during repeated processing. In this work, the degradation behaviour of acrylonitrile–butadiene–styrene (ABS) recovered from sorted WEEE streams was systematically investigated through multiple extrusion cycles, and the effectiveness of antioxidant stabilization was evaluated. Progressive degradation was assessed by chemical structure, rheological, thermal and mechanical testing, and colorimetric analysis. Repeated processing resulted in increased melt flow, altered viscoelastic behaviour, molecular weight reduction, deterioration of mechanical properties, and discoloration. To mitigate these effects, antioxidant-stabilized compounds were prepared and subjected to identical reprocessing pathways. The addition of antioxidants effectively reduced chain scission, stabilized rheological properties, and limited colour changes during reprocessing. Furthermore, the processability of the optimized recycled ABS is demonstrated through filament extrusion for fused filament fabrication, highlighting its potential for high-value additive manufacturing applications. These results demonstrate that appropriate stabilization strategies can significantly enhance the process stability and recyclability of styrenic polymers from WEEE streams, supporting their use in higher-value applications within a circular economy framework. Full article

Rare earth elements (REEs) are essential to many low-carbon and digital technologies, yet the primary supply is geographically concentrated; waste electrical and electronic equipment (WEEE) could act as an “urban mine”, but recovery pathways remain fragmented. We synthesize the evidence through a structured literature review of Scopus and Web of Science indexed studies focusing on WEEE-derived feedstocks for REE recovery: 148 records were screened and 51 papers met the inclusion criteria. Reporting of the search and study selection process follows PRISMA 2020. We coded each study by WEEE source/fraction, core technology family, and process configuration, target REEs, performance reporting, environmental proxies, and maturity, and discussed gaps against circularity goals. Results show an intense concentration on a few feedstocks, permanent magnets (22 studies), fluorescent lamps (16), and batteries (6), with only limited attention to multi-source streams. Hydrometallurgical routes dominate, while biometallurgical options are less explored. Recovery is more frequently reported than selectivity and environmental indicators, and most solutions remain at proof-of-concept maturity. Due to the heterogeneity of feedstocks, process configurations, and reported metrics, the findings were synthesized qualitatively (no meta-analysis). This review highlights priorities for future work: multi-source and heavy rare earth elements focused feedstocks, more selective and intensified flowsheets, harmonized performance reporting, and scale-up supported by life-cycle and cost assessments. Full article

The transition from a linear to a circular, resource-efficient economy is crucial in order to address the growing scarcity of resources, environmental degradation and the rapid increase in electronic waste and end-of-life products. Artificial Intelligence (AI) has emerged as a key enabling technology, capable of enhancing decision making, automation and optimization across Circular Economy (CE) pathways, including reuse, remanufacturing and recycling. This perspective paper presents a comprehensive and critical overview of AI’s role in supporting the transition to a circular, resource-efficient economy, introducing the Digital CE Architecture (DCEA-4) as a novel framework for integrating AI across the circular value chain. Recent advances in machine learning, deep learning and data-driven optimization are analyzed in the context of electronic waste and used battery management. This highlights how AI-based solutions can improve material recovery rates, reduce environmental impact and enhance system-level efficiency. Additionally, we examine major challenges concerning data availability, model generalization, industrial deployment, and explainability, together with relevant industrial case studies. Although AI offers substantial potential for optimizing circular resource systems, its environmental benefits must be balanced against the computational energy demands of large-scale AI models. This perspective discusses the potential rebound effects associated with AI deployment and emphasizes the importance of energy-efficient algorithms and sustainable digital infrastructures. By bringing together current developments and highlighting future opportunities, this paper aims to help researchers, practitioners and policymakers leverage AI to speed up the transition to sustainable, circular and resource-efficient systems. Full article

Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the collection phase. In this context, this work proposes the integration of an image-based classification framework within the Waste Electrical and Electronic Equipment (WEEE) recycling pipeline to selectively identify electric motors containing permanent magnets (PMs) and direct them toward dedicated recycling processes for rare earth recovery. The proposed methodology relies on a Discriminative Transfer Learning (DTL) approach based on a ResNeXt convolutional neural network (CNN), adapted to a proprietary and heterogeneous dataset of electric motors acquired in an industrial recycling facility. The objective is twofold: first, to identify motors containing PMs; second, to classify motors into construction categories according to their likelihood of incorporating PMs. Experimental results show promising performance in terms of PM-containing motor detection capability, establishing a robust foundation for the automated recovery of REEs at an industrial scale. Furthermore, the model’s generalization capabilities can be further enhanced through the expansion of collaborative datasets and the integration of advanced scanning technologies. Full article

The rapid increase in the production of Waste Electrical and Electronic Equipment (WEEE) and batteries requires advanced automated disassembly solutions. While disassembly automation has progressed, the non-destructive removal of electrical cable connectors (ECCs) remains a critical unresolved challenge, particularly for battery packs where safety is paramount. This paper presents a critical review of the state-of-the-art in robotic ECC disassembly. To systematically assess the technological maturity of the field, the authors introduce a functional decomposition of the process into six fundamental tasks: detection, pose estimation, accessibility, motion planning, manipulation, and extraction. While detection, pose estimation, and manipulation are more advanced due to contributions from adjacent fields like assembly and inspection, accessibility, motion planning, and extraction are still at an early stage. Based on the identified gaps, the authors suggest that future developments could follow two main directions: leveraging comprehensive databases for applications with limited variability, or shifting the disassembly approach from the connector housing to the locking mechanism to achieve broader applicability. Full article

Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from pyrolyzed industrial black mass derived primarily from LiCoO₂-based batteries, containing both LiCoO₂ and LiNiO₂ layered oxide phases. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1%–2%, w/v), inoculum volume (10–20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1% pulp density and 10% v/v inoculum, metal recoveries after 6–7 days reached about 64%–70% Co, 57%–72% Ni, 52%–60% Li and 81%–100% Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79%, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe³⁺ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO₂ batteries and provide operating windows for subsequent process optimization and scale-up. Full article

Pyrolysis of plastic from waste electrical and electronic equipment (WEEE) is a promising method for producing value-added chemicals. However, flame retardants in WEEE can cause halogen contamination in pyrolysis oil, reducing its value. This work aims to develop an innovative catalytic hydrodehalogenation (CHD) protocol for the removal of chlorobenzene and bromobenzene. Iron sulphate heptahydrate (FeSO₄·7H₂O) and nickel ammonium sulphate hexahydrate ((NH₄)₂Ni(SO₄)₂·6H₂O) were used as catalysts, while sodium borohydride (NaBH₄) acted as a hydrogen donor for iron reduction. The novelty of the process lies in the generation of nano zero-valent iron (nZVI) that takes place within the CHD reactor (in situ) without the addition of strong acids. Various experimental set-ups were investigated to optimise the key process parameters (e.g., reagent concentrations). The optimal conditions—obtained in the autoclave at 30 °C with a 1:1 molar ratio of chlorobenzene to catalyst, omission of nickel salt, and 5 mmol of NaBH₄—resulted in a 75% reduction in chlorobenzene and complete removal of bromobenzene. The results confirm the effectiveness of the proposed protocol for the dehalogenation of chlorobenzene and bromobenzene, which can facilitate the valorization of pyrolysis oils derived from plastic waste, contributing to the closure of the WEEE cycle (the widest and fastest-growing source of global waste with significant environmental, social and economic impacts). Full article

The growing volume of waste electrical and electronic equipment presents both an environmental challenge and an opportunity for recovering critical raw materials embedded in discarded products. While recycling technologies are advancing, effective recovery remains strongly constrained by upstream collection systems, particularly in urban contexts subject to uncertainty, capacity limits, and regulatory constraints. This paper examines WEEE collection as a key lever for supporting sustainable critical-metal recovery in Europe. Methodologically, the study combines a Scopus-based bibliometric mapping and an institutional analysis of EU collection arrangements with the development of a robust multi-period mixed-integer linear programming model. After analysing organisational and regulatory arrangements in Poland and Portugal as illustrative cases, the paper introduces the Robust Multi-Period WEEE Allocation and Rare Metal Accumulation Problem (MP-WARMAP). The model integrates uncertain WEEE availability, intertemporal logistics planning, threshold-based rare-metal accumulation with endogenous sale timing, and a binding transport-related emission cap. Computational experiments show that robustness against inflow uncertainty can be achieved at a relatively low economic cost, that emission regulation exhibits a feasibility-threshold effect, and that capacity constraints may dominate price signals in determining recovery timing. The results highlight the importance of collection-system design and operational feasibility for improving the recovery of critical materials from urban WEEE streams. Full article

The accelerated closure of hard coal mines across Europe contrasts with Poland’s continued structural reliance on coal extraction and coal-based power generation, increasing the urgency of credible post-mining development models. This article investigates the potential transformation of the end-of-life Bobrek coal mine in Bytom (Poland), drawing on methodological and business-model insights from the European Union (EU) Research Fund for Coal and Steel (RFCS) POTENTIALS and GreenJOBS projects. A combined methodological framework is applied, including structural analysis to identify key transformation variables, morphological analysis to explore alternative redevelopment pathways, and multicriteria assessment to configure coherent scenarios integrating renewable energy systems and circular-economy activities. The results show that an industrial eco-park backed by a virtual power plant (VPP), comprising photovoltaic installations, a mine-water-based geothermal heating system, and small-scale wind turbines, is technically feasible and environmentally sustainable. In parallel, three circular-economy business lines, the recycling of end-of-life photovoltaic panels, waste electrical and electronic equipment (WEEE), and refrigeration units, were assessed as possible economic cores of the envisaged eco-park. Overall, the proposed model enables effective reuse of mining infrastructure, supports low-emission industrial activity, and aligns with EU climate policy objectives. The Bobrek site may serve as a reference for post-mining redevelopment in other coal regions. Full article

The escalating global surge in Waste Electrical and Electronic Equipment (WEEE) necessitates the strategic deployment of recycling facilities within resilient, multi-period networks. However, existing planning methodologies falter due to the non-stationary spatiotemporal volatility of e-waste generation, the high reconfiguration costs associated with path-dependent infrastructure, and the “curse of dimensionality” inherent in large-scale dynamic optimization. To address these challenges, we propose LAtt-PR, an innovative hybrid reinforced adaptive optimization framework. The methodology integrates a spatiotemporal attention-based neural network, combining Multi-Head Attention (MHA) for spatial correlation with Long Short-Term Memory (LSTM) units for temporal dependencies to accurately capture and predict fluctuating demand patterns. At its core, the framework employs Deep Reinforcement Learning (DRL) as a high-level action proposer to prune the expansive search space, followed by a Particle Swarm Optimization (PSO) module to perform intensive local refinement, ensuring both global strategic foresight and numerical precision. Experimental results on large-scale instances with 150 nodes demonstrate that LAtt-PR significantly outperforms state-of-the-art benchmarks. Specifically, the proposed framework achieves a solution quality improvement of 76% over traditional metaheuristics Genetic Algorithm (GA)/PSO and 55% over pure DRL baselines Deep Q-Network(DQN)/Proximal Policy Optimization (PPO). Furthermore, while maintaining a negligible optimality gap of less than 4% relative to the exact solver Gurobi, LAtt-PR reduces computational time to just 16% of the solver’s requirement. These findings confirm that LAtt-PR provides a robust, scalable, and efficient decision-making tool for optimizing resource circularity and environmental resilience in volatile, real-world recycling logistics. Full article

Waste electrical and electronic equipment (WEEE) is a rapidly growing waste stream rich in precious metals, with gold in particular being concentrated in printed circuit boards and other high-value components. Historically, industrial recycling has relied on pyrometallurgy and non-selective hydrometallurgical leaching. These recovery routes have major drawbacks, including high energy demand, corrosion, the use of toxic reagents, and the complexity of pregnant leach solutions, which complicate downstream gold recovery. This review aims to synthesize recent advances in selective gold recovery from WEEE using a speciation-driven approach. Mechanical pretreatment and physical beneficiation methods are critically assessed as processes for concentrating gold and reducing the amount of material sent to downstream hydrometallurgical leaching. Different lixiviants, from conventional cyanide to halide-based, as well as greener chemistries such as thiosulfate and thiourea, are assessed for gold dissolution from the WEEE stream. Assessment of different extraction methods, including sorbents, ion exchange resins, solvent/ionic liquid, direct reduction/precipitation, and electrochemical recovery, is conducted. The review concludes with guidelines for potential process integration and highlights the need for scalable, reusable lixiviants and sorbent materials validated under realistic multi-metal conditions in real WEEE leachate. Full article

Indium is a strategically important metal, essential for the production of transparent conductive oxides, flat panel displays, thin-film photovoltaics, and advanced optoelectronic devices. Due to its limited natural abundance and its occurrence in trace amounts alongside other metals in both primary and secondary sources, the recovery of indium through efficient separation techniques has gained increasing attention. This review discusses three major separation strategies for indium recovery: solvent extraction, ion-exchange, and membrane processes, applied to both synthetic solutions and real leachates. D2EHPA has demonstrated its applicability as an effective agent for indium separation, not only in solvent extraction but also as an impregnating agent in polymer resins and membranes. While solvent extraction achieves high recovery rates, ion-exchange resins and membrane-based methods offer significant advantages in terms of reusability, reduced chemical consumption, and minimal environmental impact. The selective separation of indium from impurities such as Fe³⁺ and Sn²⁺ remains a key consideration, which can be addressed by optimizing feed solution conditions or adjusting the selective stripping stages. A comparative overview of these methods is provided, focusing on separation efficiency, operational conditions, and potential integration into close-loop systems. The article highlights recent innovations and outlines the challenges involved in achieving sustainable indium recovery, in line with circular economy principles. Full article