A Systematic Literature Review of Waste Identification in Automatic Separation Systems DOI Creative Commons
Juan Carlos Arbeláez, Paola Vallejo, José Aguilar

et al.

Recycling, Journal Year: 2023, Volume and Issue: 8(6), P. 86 - 86

Published: Nov. 2, 2023

Proper waste separation is essential for recycling. However, it can be challenging to identify materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out the physical enablers (sensors and computing devices), datasets, machine learning (ML) algorithms used identification indirect systems. This analyzed 55 studies, following Kitchenham guidelines. The SLR identified three levels of autonomy segregation systems: full, moderate, low. Edge devices are most widely data processing (9 17 studies). Five types sensors identification: inductive, capacitive, image-based, sound-based, weight-based sensors. Visible-image-based common literature. Single classification popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) commonly ML technique (24 26 articles). One main conclusions that faces challenges with complexity, limited lack detailed categorization. Future work should focus on deployment testing non-controlled environments, expanding system functionalities, exploring sensor fusion.

Language: Английский

Smart waste management: A paradigm shift enabled by artificial intelligence DOI Creative Commons
David B. Olawade, Oluwaseun Fapohunda, Ojima Z. Wada

et al.

Waste Management Bulletin, Journal Year: 2024, Volume and Issue: 2(2), P. 244 - 263

Published: May 9, 2024

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste its environmental impact. However, advent Artificial Intelligence (AI) technologies offers promising avenues tackling complexities systems. This review provides comprehensive examination AI's role management, encompassing collection, sorting, recycling, monitoring. It delineates potential benefits challenges associated with each application while emphasizing imperative improved data quality, privacy measures, cost-effectiveness, ethical considerations. Furthermore, future prospects AI integration Internet Things (IoT), advancements machine learning, importance collaborative frameworks policy initiatives were discussed. In conclusion, holds significant promise enhancing practices, such as concerns, cost implications is paramount. Through concerted efforts ongoing research endeavors, transformative can be fully harnessed to drive sustainable efficient practices.

Language: Английский

Citations

65
Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy DOI Open Access

Ali Kassab,

Dawood Al Nabhani,

Pravansu Mohanty

et al.

Polymers, Journal Year: 2023, Volume and Issue: 15(19), P. 3881 - 3881

Published: Sept. 25, 2023

The concept of the circular economy has emerged as a promising solution to address mounting concerns surrounding plastic waste and urgent need for sustainable resource management. While conventional centralized recycling remains common practice waste, facilities may prove inadequate in handling ever-increasing volumes generated globally. Consequently, exploring alternative methods, such distributed by additive manufacturing, becomes paramount. This innovative approach encompasses actively involving communities practices promotes economy. comprehensive review paper aims explore critical aspects necessary realize potential manufacturing. In this paper, our focus lies on proposing schemes that leverage existing literature harness manufacturing an effective We intricacies process, optimize 3D printing parameters, challenges, evaluate mechanical properties recycled materials. Our investigation draws heavily from last five years, we conduct thorough assessment DRAM implementation its influence structures. Through analysis, reveal materials delivering functional components, with insights into their performance, strengths, weaknesses. serves guide those interested embracing transformative recycling. By fostering community engagement, optimizing processes, incorporating suitable additives, it is possible collectively contribute more future while combatting crisis. As progress made, essential further delve complexities material behavior, techniques, long-term durability printed components. addressing these challenges head-on, feasible refine advance viable pathway minimize cultivating cleaner planet generations come.

Language: Английский

Citations

51
Accurate determination of polyethylene (PE) and polypropylene (PP) content in polyolefin blends using machine learning-assisted differential scanning calorimetry (DSC) analysis DOI Creative Commons
Amir Bashirgonbadi, Yannick Ureel, Laurens Delva

et al.

Polymer Testing, Journal Year: 2024, Volume and Issue: 131, P. 108353 - 108353

Published: Jan. 26, 2024

Polyethylene (PE) and polypropylene (PP) are among the most recycled polymers. However, these polymers present similar physicochemical characteristics cross-contamination between them is commonly observed, affecting quality of recyclates. With increasing demand for plastics, understanding composition materials crucial. Numerous techniques have been introduced in literature to determine plastics. An ideal technique should be accessible, cost-efficient, fast, accurate. Differential Scanning Calorimetry (DSC) emerges as a suitable since it analyzes thermal behavior compounds under controlled time temperature conditions, entitling quantitative determination each component, e.g., PE/PP blends. Nevertheless, existing predictive methods lack accuracy estimating blends from DSC analysis this blend affects its overall crystallinity. This study advances state-of-the-art regarding quantification using by implementing non-linear calibration curve correlating evolutions crystallinity with composition. Additionally, machine-learned (ML) model validated, achieving high determination, presenting an mean absolute error low 1.0 wt%. Notably, ML-assisted approach can also quantify content subcategory polymers, enhancing utility.

Language: Английский

Citations

25
A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste DOI Creative Commons

Rok Pučnik,

Monika Dokl, Yee Van Fan

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 141762 - 141762

Published: March 12, 2024

Plastic waste pollution is a challenging and complex issue caused mainly by high consumption of single-use plastics the linear economy "extract-make-use-throw". Improvements in recycling efficiency, behaviour changes, circular business models, more precise management system are essential to reduce volume plastic waste. This paper proposes simplified conceptual model for smart separation based on sensor technology deep learning (DL) facilitate recovery recycling. The proposed could be applied either at source (in bins) or centralised sorting facility. Two systems have been investigated: i) one utilising 6 sensors (near-infrared (NIR), humidity, temperature, CO2, CH4, laser profile sensor) ii) with an RGB camera separate packaging materials their composition, size, cleanliness, appearance. Simulations case study showed that camera-based sorting, Inception-v3, DL convolution neural networks (CNN), achieved best overall accuracy (78%) compared ResNet-50, MobileNet-v2, DenseNet-201. In addition, resulted higher number misclassified items bins, as it focused solely appearance rather than material composition. Sensor-based faced limitations, particularly dark colouration organic matter entrapment. Combining information from cameras potentially mitigate limitations each individual method, thus resulting purity separated fractions.

Language: Английский

Citations

23
Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots DOI Creative Commons

Cesar Lubongo,

Mohammed A. A. Bin Daej, Paschalis Alexandridis

et al.

Recycling, Journal Year: 2024, Volume and Issue: 9(4), P. 59 - 59

Published: July 15, 2024

Plastics recycling is an important component of the circular economy. In mechanical recycling, recovery high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. chemical certain types should removed as they negatively affect process. Such sortation objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical any sorting proper identification type. Spectroscopy used this end, augmented machine learning (ML) artificial intelligence (AI). Recent developments in application ML/AI are highlighted here, state art presented. Commercial equipment recyclables identified from a survey publicly available information. Automated equipment, ML/AI-based sorters, robotic sorters currently market evaluated regarding their sensors, capability sort plastics, primary application, throughput, accuracy. This information reflects rapid progress achieved plastics. However, film, dark comprising multiple polymers remains challenging. Improvements and/or new solutions forthcoming.

Language: Английский

Citations

20
Economic and environmental analysis of plastics pyrolysis after secondary sortation of mixed plastic waste DOI Creative Commons
Daniel G. Kulas, Ali Zolghadr, Utkarsh S. Chaudhari

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 384, P. 135542 - 135542

Published: Dec. 6, 2022

Language: Английский

Citations

47
Recent advances in polyvinyl chloride (PVC) recycling DOI
Zouhair Ait‐Touchente,

Maya Khellaf,

Guy Raffin

et al.

Polymers for Advanced Technologies, Journal Year: 2023, Volume and Issue: 35(1)

Published: Nov. 2, 2023

Abstract Polyvinyl chloride (PVC) recycling is crucial for mitigating the environmental impact of PVC wastes, which take decades to decompose in landfills. This review examines current state processes, focusing on challenges and future research opportunities. It explores types sources including post‐consumer, industrial, construction wastes. Conventional methods such as mechanical, thermal, chemical are discussed, highlighting their advantages, limitations, successful applications. Furthermore, recent advances recycling, biological, plasma‐assisted, solvent‐based explored, considering potential benefits challenges. The emphasizes European context region has implemented regulatory initiatives collaborations. points out Circular Economy Action Plan directives targeting waste management, have promoted established a supportive framework. Challenges technologies, low yield high energy consumption, identified. calls development efficient cost‐effective along with improvements infrastructure consumer awareness. Assessing economic impacts, significantly reduces greenhouse gas emissions conserves resources compared virgin production. include job creation reduced raw material costs.

Language: Английский

Citations

41
Untangling the chemical complexity of plastics to improve life cycle outcomes DOI
Kara Lavender Law, Margaret J. Sobkowicz, Michael P. Shaver

et al.

Nature Reviews Materials, Journal Year: 2024, Volume and Issue: 9(9), P. 657 - 667

Published: Aug. 13, 2024

Language: Английский

Citations

16
Recovery of plastic packaging from mixed municipal solid waste. A case study from Austria DOI Creative Commons
Dominik Blasenbauer, Anna-Maria Lipp, Johann Fellner

et al.

Waste Management, Journal Year: 2024, Volume and Issue: 180, P. 9 - 22

Published: March 18, 2024

Austria must recycle more packaging materials. Especially for plastic waste, significant increases are necessary to reach the EU recycling targets 2025 and 2030. In addition improving separate collection introducing a deposit system specific fractions, share of in mixed municipal solid waste (MSW) could be utilized. Austria, about 1.8 million tonnes MSW generated. This includes 110,000 t/a waste. Most (94 %) is sent directly or via residues from pre-treatment, such as mechanical–biological treatment sorting, incineration. While materials glass metals can also recovered bottom ash, combustible plastics before work aims evaluate recovery potential with automated sorting. For this purpose, two largest Austrian sorting plants, total annual throughput 280,000 t/a, were investigated. The investigation included regular sampling selected output streams analysis. results show that theoretical these plants 6,500 on average. An extrapolation 83,000 t/a. If losses due further treatment, recycling, considered, 30,000 recyclate returned production. would correspond an increase rate 25 % 35 %.

Language: Английский

Citations

11
Recycling of Blended Fabrics for a Circular Economy of Textiles: Separation of Cotton, Polyester, and Elastane Fibers DOI Open Access

Khaliquzzaman Choudhury,

Marina Tsianou, Paschalis Alexandridis

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(14), P. 6206 - 6206

Published: July 20, 2024

The growing textile industry is polluting the environment and producing waste at an alarming rate. wasteful consumption of fast fashion has made problem worse. management textiles been ineffective. Spurred by urgency reducing environmental footprint textiles, this review examines advances challenges to separate important constituents such as cotton (which mostly cellulose), polyester (polyethylene terephthalate), elastane, also known spandex (polyurethane), from blended textiles. Once separated, individual fiber types can meet demand for sustainable strategies in recycling. concepts mechanical, chemical, biological recycling are introduced first. Blended or mixed pose mechanical which cannot fibers blend. However, separation blends be achieved molecular recycling, i.e., selectively dissolving depolymerizing specific polymers Specifically, through dissolution, acidic hydrolysis, acid-catalyzed hydrothermal treatment, enzymatic hydrolysis discussed here, followed elastane other selective degradation dissolution elastane. information synthesized analyzed assist stakeholders sectors mapping out achieving practices promoting shift towards a circular economy.

Language: Английский

Citations

11