An automatic sorting system for electronic components detached from waste printed circuit boards

Bioplastic packaging in circular economy: A systems-based policy approach for multi-sectoral challenges

Bioplastics have long been publicized as a sustainable plastic packaging alternative; however, their widespread industrialization is still embryonic due to complex challenges spanning multiple sectors. This review critically analyses the bioplastic lifecycle and provides a holistic evaluation of both the opportunities and potential trade-offs along their value chain. Their lifecycle is divided into three sectors: 1) resources, extraction, and manufacturing, 2) product consumption which discusses availability, consumer perception, and marketing strategies, and 3) end-of-life (EoL) management which includes segregation, recycling, and disposal. In the production phase, the primary challenges include selection of suitable raw feedstocks and addressing the techno-economic constraints of manufacturing processes. To tackle these challenges, it is recommended to source sustainable feedstocks from innovative, renewable, and waste materials, adopt green synthesis mechanisms, and optimize processes for improved efficiency. The consumption phase encompasses challenges related to market availability, cost competitiveness, and consumer perception of bioplastics. Localizing feedstock sourcing and production, leveraging the economics of scale, and promoting market demand for recycled bioplastics can positively influence the market dynamics. Additionally, dispelling misconceptions about degradability through proper labeling, and employing innovative marketing strategies to enhance consumer perception of the mechanical performance and quality of bioplastics is crucial. During the EoL management phase, major challenges include inadequate awareness, inefficient segregation protocols, and bioplastics with diverse properties that are incompatible with existing waste management infrastructure. Implementing a standardized labeling system with clear representation of suitable EoL techniques and integrating sensors and machine learning-based sorting technologies will improve segregation efficiency. Further, establishing interconnected recycling streams that clearly define the EoL pathways for different bioplastics is essential to ensure circular waste management systems. Finally, designing a comprehensive systems-based policy framework that incorporates technical, economic, environmental, and social drivers is recommended to promote bioplastics as a viable circular packaging solution.

Drivers and Pathways for the Recovery of Critical Metals from Waste-Printed Circuit Boards

The ever-increasing importance of critical metals (CMs) in modern society underscores their resource security and circularity. Waste-printed circuit boards (WPCBs) are particularly attractive reservoirs of CMs due to their gamut CM embedding and ubiquitous presence. However, the recovery of most CMs is out of reach from current metal-centric recycling industries, resulting in a flood loss of refined CMs. Here, 41 types of such spent CMs are identified. To deliver a higher level of CM sustainability, this work provides an insightful overview of paradigm-shifting pathways for CM recovery from WPCBs that have been developed in recent years. As a crucial starting entropy-decreasing step, various strategies of metal enrichment are compared, and the deployment of artificial intelligence (AI) and hyperspectral sensing is highlighted. Then, tailored metal recycling schemes are presented for the platinum group, rare earth, and refractory metals, with emphasis on greener metallurgical methods contributing to transforming CMs into marketable products. In addition, due to the vital nexus of CMs between the environment and energy sectors, the upcycling of CMs into electro-/photo-chemical catalysts for green fuel synthesis is proposed to extend the recycling chain. Finally, the challenges and outlook on this all-round upgrading of WPCB recycling are outlined.

Physicochemical reactions in e-waste recycling

Electronic waste (e-waste) recycling is becoming a global concern owing to its immense quantity, hazardous character and the potential loss of valuable metals. The many processes involved in e-waste recycling stem from a mixture of physicochemical reactions, and understanding the principles of these reactions can lead to more efficient recycling methods. In this Review, we discuss the principles behind photochemistry, thermochemistry, mechanochemistry, electrochemistry and sonochemistry for metal recovery, polymer decomposition and pollutant elimination from e-waste. We also discuss how these processes induce or improve reaction rates, selectivity and controllability of e-waste recycling based on thermodynamics and kinetics, free radicals, chemical bond energy, electrical potential regulation and more. Lastly, key factors, limitations and suggestions for improvements of these physicochemical reactions for e-waste recycling are highlighted, wherein we also indicate possible research directions for the future.

DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection

In a global context, the production of urban solid waste significantly varies with changes in living standards. This trend exhibits diversity across different countries and regions, reflecting shifts in lifestyles as well as varying needs and challenges in waste management strategies. However, current standards of waste recycling are too complex for the general public to follow. In this study, we propose a model called DSYOLO-Trash to identify solid waste by integrating the dual attention mechanisms convolutional block attention module (CBAM) and Contextual Transformer Networks(CotNet), which significantly enhance its ability to mine channel-related and spatial attention features while optimizing the learning process. We apply the deep simple online and realtime tracking (DeepSORT) object tracking algorithm to solid waste detection for the first time in the literature to enable the real-time identification and tracking of waste. We also develop a multi-label dataset of mixed solid waste, called MMTrash, to realistically simulate actual scenarios of waste classification. Our proposed DSYOLO-Trash delivered superior performance to classical detection algorithms on both the MMTrash and the TrashNet datasets. Our system combines the improved you only look once(YOLO) algorithm with DeepSORT technology by using industrial cameras and PLC-controlled robotic arms to intelligently sort waste. The work here constitutes an important contribution to intelligent waste management and the sustainable development of cities.

A computer vision-based system for real-time component identification from waste printed circuit boards

With an exponential increase in consumers' need for electronic products, the world is facing an ever-increasing economic and environmental threat of electronic waste (e-waste). To minimize their adverse effects, e-waste recycling is one of the pivotal factors that can help in minimizing the environmental pollution andto increase recovery of valuable materials. For instance, Printed Circuit Boards (PCBs), while they have several valuable elements, they are hazardous too; and therefore, they form a large chunk of e-waste being generated today. Thus, in recycling PCBs, Electronic Components (ECs) are segregated at first, and separately processed for recovering key elements that could be re-used. However, in the current recycling process, especially in developing nations, humans manually screen ECs, which goes on to affect their health. It also causes losses of valuable materials. Therefore, automated solutions need to be adopted for both to classify and to segregate ECs from waste PCBs. The study proposes a robust EC identification system based on computer vision and deep learning algorithms (YOLOv3) to automate sorting process which would help in further processing. The study uses a publicly available dataset, and a PCB dataset which reflect challenging recycling environments like lighting conditions, cast shadows, orientations, viewpoints, and different cameras/resolutions. The outcome of YOLOv3 detection model based on training of both datasets presents satisfactory classification accuracy and capability of real-time competent identification, which in turn, could help in automatically segregating ECs, while leading towards effective e-waste recycling.

Intelligent identification of fragmented non-magnetic materials for end-of-life refrigerator recycling

E-waste is a valuable secondary resource containing numerous toxic substances and high-value components. If improperly handled, it will cause severe environmental pollution. Therefore, efficient recycling of this material can reduce environmental pollution. However, after crushing, fine crushing, and magnetic separation, a substantial quantity of fragmented non-magnetic materials with high value, such as copper andg aluminum, remain. Refrigerators, as typical e-waste, have a similar composition to fragmented non-magnetic materials. Consequently, this paper focuses on the issues of low efficiency, environmental pollution, and resource waste in sorting fragmented non-magnetic materials from waste refrigerators. This paper constructs a data set of fragmented non-magnetic materials of refrigerators, augments the data set, and identifies fragmented non-magnetic materials of refrigerators using a computer vision-based deep learning method. In this study, YOLOv5s is used as the benchmark model. The CBAM module is added to the backbone to enable intelligent identification and sorting of fragmented non-magnetic materials in refrigerators. The final identification efficiency of waste refrigerators meets the requirements of industrial applications, with an accuracy rate of 98.3%, a recall rate of 96.8%, and an average accuracy of 98%. Based on the similarity of the composition of e-waste fragmented materials, this model sorting method can be applied to sorting additional e-waste fragmented materials. Furthermore, it provides the theoretical foundation for promoting e-waste resourcefulness.Implications: This paper proposes a recognition model based on YOLOv5s to solve the problems of low sorting efficiency, environmental pollution, harm to health, and resource waste of non-magnetic crushed material from refrigerators. The recognition model principally addresses the following issues: a deep learning model is developed for recognition and sorting to improve e-waste recognition and sorting efficiency. Concerning the issue of environmental benefits in an ecological environment, a vision-based automatic identification method is proposed to sort harmful waste, such as foam, to preserve the ecological environment. In response to the problem of resource waste, this project improves the purity of precious metals, resulting in a recovery rate of 99.1% for copper and 96.44% for aluminum. In other words, the cost of recovering metals has increased. The identification model of non-magnetic crushed material in refrigerators satisfies production identification and sorting requirements. In addition, the method has application and promotion value, sorting a theoretical foundation and method for identifying and classifying e-waste.

Separation and recovery of arsenic, germanium and tungsten from toxic coal ash from lignite by sequential vacuum distillation with disulphide

Large amount of coal ash is produced as industrial waste during the electricity generation through the combustion of lignite. Toxic elements arsenic exists in the coal ash, which hinders the subsequent recycling processes. Moreover, coal ash could be recycled further to retrieve scattered metals germanium and tungsten. It is believed that traditional recycling methods present barriers to scaled application, especially serious secondary pollution, such as toxic residue and waste liquid. In this work, a novel sequential vacuum distillation with disulphide method is proposed to separate arsenic, germanium and tungsten from coal ash. First, arsenic can be volatilized completely out of the reaction system at temperatures below 550 °C. Subsequently, Ge and W volatilized in the form of sulfide in the presence of Na2S2O3. The optimal condition was 1050 °C, the mass ratio of 0.6 with reaction a pressure of 1 Pa and a time duration of 120 min demonstrated the best evaporation ratio. For coal fly ash, chemical species As2S3, GeS, and WOx (x < 3)/WS2 were the main condensed products. For coal bottom ash, As2S3, GeS, and WO3/WS2 were dominant chemical components. Mechanisms for the process of release and evaporation of As, Ge, and W from coal ash, vacuum reaction, evaporation, and condensation were analyzed. In summary, the vacuum distillation method deserves to be further developed as it provides an eco-friendly method to recycle coal ash.

Review on the gentle hydrometallurgical treatment of WPCBs: Sustainable and selective gradient process for multiple valuable metals recovery

The metal resource crisis and the inherent need for a low-carbon circular economy have driven the rapid development of e-waste recycling technology. High-value waste printed circuit boards (WPCBs) are an essential component of e-waste. However, WPCBs are considered hazardous to the ecosystem due to the presence of heavy metals and brominated organic polymers. Therefore, achieving the recycling of metals in WPCBs is not only a strategic requirement for building a green ecological civilization but also an essential guarantee for achieving a safe supply of mineral resources. This review systematically analyzes the hydrometallurgical technology of metals in WPCBs in recent years. Firstly, the different unit operations of pretreatment in the hydrometallurgical process, which contain disassembly, crushing, and pre-enrichment, were analyzed. Secondly, environmentally friendly hydrometallurgical leaching systems and high-value product regeneration technologies used in recent years to recover metals from WPCBs were evaluated. The leaching techniques, including cyanidation, halide, thiourea, and thiosulfate for precious metals, and inorganic acid, organic acid, and other leaching methods for base metals such as copper and nickel in WPCBs, were outlined, and the leaching performance and greenness of each leaching system were summarized and analyzed. Eventually, based on the advantages of each leaching system and the differences in chemical properties of metals in WPCBs, an integrated and multi-gradient green process for the recovery of WPCBs was proposed, which provides a sustainable pathway for the recovery of metals in WPCBs. This paper provides a reference for realizing the gradient hydrometallurgical recovery of metals from WPCBs to promote the recycling metal resources.

High Value-Added Reutilization of Waste-Printed Circuit Boards Non-Metallic Components in Sustainable Polymer Composites

The reutilization non-metallic components from a waste-printed circuit board (WPCB) has become one of the most significant bottlenecks in the comprehensive reuse of electronic wastes due to its low value and complex compositions, and it has received great attention from scientific and industrial researchers. To effectively address the environmental pollution caused by inappropriate recycling methods, such as incineration and landfill, extensive efforts have been dedicated to achieving the high value-added reutilization of WPCB non-metals in sustainable polymer composites. In this review, recent progress in developing sustainable polymer composites based on WPCB non-metallic components was systematically summarized. It has been demonstrated that the WPCB non-metals can serve as a promising reinforcing and functional fillers to significantly ameliorate some of the physical and chemical properties of polymer composites, such as excellent mechanical properties, enhanced thermal stability, and flame retardancy. The recovery strategies and composition of WPCB non-metals were also briefly discussed. Finally, the future potentials and remaining challenges regarding the reutilization of WPCB non-metallic components are outlined. This work provides readers with a comprehensive understanding of the preparation, structure, and properties of the polymer composites based on WPCB non-metals, providing significant insights regarding the high value-added reutilization of WPCB non-metals of electronic wastes.

Enhanced bromine fixation and tar lightweighting in co-pyrolysis of non-metallic fractions of waste printed circuit boards with Bayer red mud

A co-pyrolysis process for non-metallic fractions (NMFs) from WPCBs with Bayer red mud (RM) is proposed to upgrade pyrolysis products in this study. High bromine fixation efficiency was realized, and higher content of lightweight pyrolysis tar was obtained. The mechanism of catalytic pyrolysis and simultaneous bromine fixation of NMFs by RM was investigated by experiments and theoretical calculations. The three inorganic components of Fe₂O₃, CaCO₃ and Al₂O₃ in RM played key roles in the catalytic pyrolysis of NMFs, and their order of catalytic debromination effect was CaCO₃ > Fe₂O₃ > Al₂O₃. By adding 15 wt% RM, the pyrolysis solid residue could fix 89.55 wt% bromine, compared with 35.42 wt% of NMFs without adding RM, due to the formation of FeBr₂ and CaBr₂ from Fe₂O₃ and CaCO₃ in RM, respectively. Tar lightweighting was realized by reducing the energy barrier of the direct decomposition of tetrabromobisphenol A (TBBPA) in NMFs. The order of effect of the three key components on the tar lightweighting was Fe₂O₃ > Al₂O₃ > CaCO₃. The content of lightweight tar in the tar obtained by catalytic pyrolysis of NMFs with 15 wt% RM was 44.29% higher than that in the tar obtained by direct pyrolysis of NMFs. This work provides a theoretical guidance for the low-cost and eco-friendly recycling of e-wastes by co-pyrolysis with RM.

E-waste management, treatment options and the impact of heavy metal extraction from e-waste on human health: Scenario in Vietnam and other countries

Ho Chi Minh (HCM) City is the most important urban region of Vietnam, Southeast Asia. In recent times, the quantity of electronic waste (e-waste) has been growing by several thousand tonnes every year. In this research, some of the existing and developing technologies being employed for the recycling of e-waste have been reviewed. Accordingly, the paper has been divided into three sections namely, e-waste treatment technologies in Ho Chi Minh City, the effect of heavy metals on human health and the extraction of metals from e-waste using pyrolysis, hydrometallurgy, bioleaching, mechanical, and air classifier methods, respectively. The extraction of precious metals and heavy metals such as Cd, Cr, Pb, Hg, Cu, Se, and Zn from e-waste can be hazardous to human health. For example, lead causes hazards to the central and peripheral nervous systems, blood system and kidneys; copper causes liver damage; chronic exposure to cadmium ends up causing lung cancer and kidney damage, and mercury can cause brain damage. Thus, this study examines the key findings of many research and review articles published in the field of e-waste management and the health impacts of metal pollution.

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

Applications of crushing and grinding-based treatments for typical metal-containing solid wastes: Detoxification and resource recovery potentials

Metal-containing solid wastes can induce serious environmental pollution if managed improperly, but contain considerable resources. The detoxification and resource recoveries of these wastes are of both environmental and economic significances, being indispensable for circular economy. In the past decades, attempts have been made worldwide to treat these wastes. Crushing and grinding-based treatments have been increasingly applied, the operating apparatus and parameters of which depend on the waste type and treatment purpose. Based on the relevant studies, the applications of crushing and grinding on four major types of solid wastes, namely spent lithium-ion batteries (LIBs) cathode, waste printed circuit boards (WPCBs), incineration bottom ash (IBA), and incineration fly ash (IFA) are here systematically reviewed. These types of solid wastes are generated in increasing amounts, and have the potentials to release various organic and inorganic pollutants. Despite of the widely different texture, composition, and other physicochemical properties of the solid wastes, crushing and grinding have been demonstrated to be universally applicable. For each of the four wastes, the technical route that involving crushing and grinding is described with the advantages highlighted. The crushing and grinding serve either mainstream or auxiliary role in the processing of the solid wastes. This review summarizes and highlights the developments and future directions of crushing and grinding-based treatments.

Urgency of technology and equipment upgrades in e-waste dismantling base: Pollution identification and emission reduction

Recycling of electronic waste (e-waste) and inevitable pollution under current technology have always been a concern of people. Generation and release of pollutants in the recycling process of e-waste are closely related to processing technology and equipment. In this paper, the pollution characteristics of different functional areas and critical processing units in formal e-waste dismantling base have been studied systematically and comprehensively. The results showed that the overall pollutants concentration in crushing workshop and cathode ray tube (CRT) monitor disposing workshop are much higher than other functional areas. Screen-cone glass separation for CRT monitor was the processing unit with the greatest exposure risk and the hazard index (HI) of Pb was 4.60. Pollutant emission factor of the main processing units was calculated and the waste printed circuit board (WPCB) crushing was the most polluted unit. Appropriate improvements in technology and equipment can effectively reduce the generation and release of pollutants. Some reasonable prospects about intelligent equipment and special technologies were proposed for e-waste disposal. All the results provided theoretical and data support for pollution control and technology upgrade of the formal e-waste dismantling base.

Metals extraction processes from electronic waste: constraints and opportunities

The skyrocketing demand and progressive technology have increased our dependency on electrical and electronic devices. However, the life span of these devices has been shortened because of rapid scientific expansions. Hence, massive volumes of electronic waste (e-waste) is generating day by day. Nevertheless, the ongoing management of e-waste has emerged as a major threat to sustainable economic development worldwide. In general, e-waste contains several toxic substances such as metals, plastics, and refractory oxides. Metals, particularly lead, mercury, nickel, cadmium, and copper along with some valuable metals such as rare earth metals, platinum group elements, alkaline and radioactive metal are very common; which can be extracted before disposing of the e-waste for reuse. In addition, many of these metals are hazardous. Therefore, e-waste management is an essential issue. In this study, we critically have reviewed the existing extraction processes and compared among different processes such as physical, biological, supercritical fluid technologies, pyro and hydrometallurgical, and hybrid methods used for metals extraction from e-waste. The review indicates that although each method has particular merits but hybrid methods are eco-friendlier with extraction efficiency > 90%. This study also provides insight into the technical challenges to the practical realization of metals extraction from e-waste sources.

Balanced-YOLOv3: Addressing the Imbalance Problem of Object Detection in PCB Assembly Scene

The object detection algorithm of the PCB (Printed Circuit Board) assembly scene based on CNN (Convolutional Neural Network) can significantly improve the production capacity of intelligent manufacturing of electronic products. However, the object class imbalance in the PCB assembly scene, the multi-scale feature imbalance, and the positive/negative sample imbalance in the CNN have become critical problems restricting object detection performance. Based on YOLOv3, this paper proposes a class-balanced Train/Val (Training set/Validation set) split method for object class imbalance, an additional feature fusion strategy for multi-scale feature imbalance, and an efficient anchor concept for positive/negative sample imbalance. These three contributions are Balanced-YOLOv3. After experimental verification, compared with other YOLOv3 series algorithms, the mAP@.5 (Mean Average Precision at Intersection over Union threshold 0.5) and mAP@.5:.95 (average mAP over different Intersection over Union thresholds, from 0.5 to 0.95, step 0.05) of Balanced-YOLOv3 have achieved the best results and ranked third in the metrics of parameter and inference time. Compared with other current anchor-based object detection algorithms, Balanced-YOLOv3 has excellent detection performance and low computational complexity, which effectively solves the problem of imbalanced object detection in PCB assembly scenarios.