Hasil untuk "Standardization. Simplification. Waste"

Madina Shamsuyeva, Hans-Josef Endres

Abstract The purpose of this study is to review existing recycling technologies, standards and market situation for plastics recycling. The principal results show that mechanical recycling is the most well-developed recycling approach in terms of industrial feasibility . This approach enables development of plastic recyclates of various quality levels . At the same time, transfer of many research findings into practice is hindered due to the global plastic material flow, strongly differing regional waste management systems and lack of international recycling standards. This review shows that the development of a Circular Economy Model for plastics products requires close cooperation of scientists with standardization committees and industry.

Changshi Zhou, Feng Luan, Jiarui Hu et al.

Garment manipulation is a significant challenge for robots due to the complex dynamics and potential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened garments, which could significantly simplify downstream tasks like folding, ironing, and packing. This paper presents APS-Net, a novel approach to garment manipulation that combines unfolding and standardization in a unified framework. APS-Net employs a dual-arm, multi-primitive policy with dynamic fling to quickly unfold crumpled garments and pick-and-place (p and p) for precise alignment. The purpose of garment standardization during unfolding involves not only maximizing surface coverage but also aligning the garment's shape and orientation to predefined requirements. To guide effective robot learning, we introduce a novel factorized reward function for standardization, which incorporates garment coverage (Cov), keypoint distance (KD), and intersection-over-union (IoU) metrics. Additionally, we introduce a spatial action mask and an Action Optimized Module to improve unfolding efficiency by selecting actions and operation points effectively. In simulation, APS-Net outperforms state-of-the-art methods for long sleeves, achieving 3.9 percent better coverage, 5.2 percent higher IoU, and a 0.14 decrease in KD (7.09 percent relative reduction). Real-world folding tasks further demonstrate that standardization simplifies the folding process. Project page: see https://hellohaia.github.io/APS/

N. Bernaert, Evy De Rycke, Marijke Hunninck et al.

Food waste presents a considerable challenge to many residential facilities for the elderly. This issue is highly relevant to food costs and sustainability concerns as well as residents' nutritional needs. In the present study, food waste was determined in 13 wards of eight residential facilities for elderly people in Flanders. Plate and preparation waste at lunchtime (including soup and dessert) was accurately measured for 247 residents during five days. Plate waste for breakfast and dinner was monitored using a standardized visual estimation protocol. The mean plate waste per person was 112.3 ± 35.2 g/day, corresponding to 10.9 % of the total meal served on the plate. Bread, toppings, meat/fish and starch components were the main meal items left over. At breakfast and lunch, the meal item, amount served, gender, Mini Nutritional Assessment (MNA) score and age category significantly influenced the amount of plate waste. For dessert served at lunchtime, only the type of ward (open vs. closed) and gender (male vs. female) significantly influenced the percentage of waste. For plate waste at dinner, the meal item, amount served and gender had a significant impact. The preparation waste at lunch averaged 37.8 ± 24.3 % of the quantity prepared, or per person 192.6 ± 82.6 g/day. This preparation waste appeared to be significantly influenced by the meal consistency, meal item, and care center. The results of this study underline the need for improved food service strategies in care institutions to better match residents' nutritional requirements while reducing food waste.

A. Maalouf, Amaia Garcia-Tabar, Ana Maria Rodrigues Costa de Castro et al.

Waste minimisation and prevention are crucial for the circular economy (CE), sustainable development goals (SDGs) and environmental, social and governance (ESG) principles, focusing on waste elimination and resource efficiency. However, there are significant gaps in implementing effective waste minimisation strategies, mainly due to the lack of standardised waste prevention terminologies and indicators. This article introduces a novel simplified and comprehensive framework for assessing waste prevention and minimisation measures tailored to developing countries. The primary contribution of this study lies in proposing relevant indicators aligned with the SDGs, ESG standards, and CE principles, while addressing data scarcity through proxy indicators to enable effective assessment in resource-limited settings. Six key indicators were proposed: Zero Waste Index, Food Loss Index, Extended producer responsibility, Education and awareness programmes for waste minimisation, Waste prevention and Plastic Bag Reduction Ratio. Eleven countries were selected as case studies to demonstrate the framework’s applicability. The findings reveal that while these countries are progressing in enacting legislation and recognising the importance of waste prevention, compliance in practice is lacking, as indicated by poor quantitative results in actual waste reduction and diversion. The framework evaluates the environmental, social and economic implications of waste prevention measures, showing wide variations among countries. Each country faces unique challenges, but strengthening policy frameworks, investing in infrastructure, promoting public awareness and fostering collaboration are key steps towards advancing sustainable waste management practices. The study highlights the necessity for tailored policies addressing specific weaknesses while ensuring economic viability. The integrated framework provides actionable insights and forward-thinking solutions that can be adapted, scaled and replicated to address developing nations’ unique challenges.

Lei Hu, Zhibo Zhang, Ziwen Yuan et al.

Landfilled municipal solid waste incineration (MSWI) ash exhibits complex heterogeneity in in-situ geotechnical properties due to its spatially varying composition and long-term physicochemical transformation, e.g., stiffness increase induced by pozzolanic reactions. The heterogeneity of in-situ stiffness of landfilled MSWI ash poses challenges for landfill stability analysis, excavation planning, and long-term maintenance; however, field-based studies on this issue remain scarce. This study presents a comprehensive site investigation of an equatorial offshore MSWI ash landfill using apparent shear-wave velocity (AVs) imaging, standard penetration test, and geochemical analysis. For the first time, apparent shear-wave velocity (AVs) was employed for landfilled MSWI ash mapping, with its accuracy quantitatively evaluated. The resulting 3D AVs maps revealed distinct stiffness zones and the presence of naturally formed hard layers. A simplified stiffness classification framework was applied to segment the landfill into zones relevant for engineering planning. The segmented zones show layered patterns. Supporting geochemical analyses identified high levels of pozzolanic elements (e.g., Ca, Si, Al, Fe), elevated pH, and persistent moisture, consistent with pozzolanic conditions that promote in-situ cementation. The study provides a methodological framework for stiffness heterogeneity characterization of MSWI ash landfills, offering valuable insight into applied waste management.

Morteza Jalali Alenjareghi, Samira Keivanpour, Y. Chinniah et al.

Shengya Gao, Jiang-shan Li, Shipeng Zhang et al.

Recovery and recycling are essential strategies in solid waste management, but the potential leaching of harmful substances poses significant environmental concerns. This review examines the framework, methodologies, and mechanisms of leaching tests, focusing on mass transport (e.g., diffusion, convection) and thermodynamic restrictions (e.g., solubility, adsorption-precipitation) that influence contaminant release. The classification of leaching tests into batch, column, and pH-dependent methods highlights the need for selecting appropriate protocols based on waste properties, with batch tests for short-term assessments, column tests for long-term behaviour, and pH-dependent tests for simulating varied environmental scenarios. Future leaching studies should incorporate climate change variables, such as rainfall patterns and temperature fluctuations, to enhance the predictive reliability of contaminant mobility. Standardizing leaching protocols across regions is critical to ensuring consistent results, supporting sustainable waste management strategies globally.

Malak Anshassi

Waste management decision makers often rely on LCA findings to determine effective strategies to reduce environmental impacts, of which climate change mitigation has become centerstage. The complexity of conducting an LCA for waste management decision making is typically simplified using comprehensive models developed for wide region (e.g., United States, United Kingdom, Denmark) containing geographic and temporal metadata particular to the region. The aims of this study are to: 1) determine hotspot assumptions triggering the greatest sensitivity to the global warming potential (GWP) indicator for the management of various waste components in the US; and 2) inform on data collection approaches decision makers may use to improve their waste LCA by applying the findings of the first aim to a US context. A perturbation analysis was conducted for several recycling, landfilling, and combusting parameters using the Solid Waste Optimization Framework (SWOLF) Model. For landfilling, critical assumptions included landfill gas management factors such as lifetime gas collection efficiency, the type of gas management employed, and the bulk decay rate. In recycling, the most influential factor was the material substitution ratio. For combustion, key parameters were the avoided emissions from the electrical grid mixture and the types of metals recovered from the ash. Whenever data is available it should be supplemented in place of defaults to reduce uncertainty in waste LCA tools, especially the parameters highlighted that have influential impacts on results.

Surajet Khonjun, Rapeepan Pitakaso, Thanatkij Srichok et al.

The complex nature of pharmaceutical and biomedical waste poses significant challenges for effective management, particularly in the safe and cost-intensive disposal of infectious materials. This research presents a novel classification model that utilizes a double heterogeneous ensemble integrating deep learning, reinforcement learning, and differential evolution algorithms for waste classification. The model operates through three key phases: image augmentation, ensemble image segmentation, and ensemble convolutional neural network architectures, employing decision fusion techniques that incorporate reinforcement learning and differential evolution. It integrates various image segmentation methods, including U-Net, Mask R-CNN, DeepLab Version 3 Plus, and convolutional neural network architectures such as Inception Version 3, Residual Network 50, Mobile Network Version 2, and Densely Connected Convolutional Network 121.The developed model powers the "Biosorter," a machine specifically designed to differentiate between infectious and non-infectious waste. Comprehensive evaluations conducted on both proprietary and benchmark datasets demonstrate that the proposed BioSorter model significantly outperforms several widely used deep learning architectures-including ResNet50, DenseNet121, MobileNetV2, InceptionV3, and ResNeXt-50. On average, the model achieved classification improvements of 5.35% and 9.05% in accuracy over these methods on the respective datasets. During real-world deployment at a small medical center, the BioSorter achieved 98% sorting accuracy and a 50% increase in processing throughput. Furthermore, a post-deployment usability assessment was conducted using the System Usability Scale (SUS)-a standardized questionnaire commonly used to evaluate perceived ease of use of interactive systems. The BioSorter received a score of 93.5 on the SUS (out of 100), reflecting a high level of user-perceived efficiency and interface simplicity during operational use. This study represents a significant advancement in waste management technology, offering potential to reduce disposal costs and enhance sustainability in healthcare environments.

Tanveer Nabbu Sheikh, V. P. Bhange, M. Soni

Antibiotic resistance is a growing global concern, with municipal solid waste (MSW) landfills recognized as significant reservoirs of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). This review focuses on the emergence and spread of antibiotic resistance within landfill ecosystems and its consequent environmental impacts. It synthesizes research findings from 2018 to 2024, highlighting the persistence of resistance genes in landfill leachate, soil, air, and water. Particular emphasis is placed on the role of landfill environmental conditions in facilitating horizontal gene transfer and enhancing microbial adaptation. The review discusses how ARGs enter MSW landfills primarily through pharmaceutical waste, health care activities, agricultural runoff, and industrial discharges. Additionally, it examines current mitigation strategies, including engineered landfill designs, bioremediation using microbial consortia, development of environmentally sustainable pharmaceuticals, and enforcement of global regulatory frameworks. The analysis identifies critical research gaps, particularly in low- and middle-income countries, underscoring the urgent need for enhanced surveillance, standardized monitoring, and stringent regulations. The study recommends a multidisciplinary approach that integrates expertise from waste management, environmental science, microbiology, and public health to effectively tackle antibiotic resistance in landfill environments.

Debapam Saha, Mrutyunjay Padhiary, Azmirul Hoque et al.

It is estimated that over 1.3 billion tons of waste are generated annually from food processing, which poses significant environmental and economic challenges. This review delineates the potential of 3D printing technology in valorizing food waste and explores an achievable reduction of 40–60 % in waste disposal through product innovation. This method allows nutrient-rich waste materials like fruit peels, vegetable waste, shellfish shells, and cereal byproducts to be converted into edible and biodegradable packaging aligned with circular economy principles and sustainable food systems. Advances in 3D printing parameters, including optimized extrusion temperature and nozzle diameter, have been shown to improve efficiency by up to 30 % and the quality and integrity of the final product. Such applications are fiber-enriched snack foods and protein-enriched products with 20–35 % nutrient increases, along with biodegradable packaging that breaks down 50 % faster than conventional plastic. Case studies reveal that implementing such solutions by food manufacturers can generate as much as 25 % savings in waste management costs. These advancements are, however, challenged, especially concerning material variability, printability, and regulatory compliance. Existing studies have primarily focused on material formulation and extrusion properties, but gaps persist in large-scale implementation, standardization, and economic feasibility. Future research should emphasize AI-driven optimization to enhance printability by 15–20 %, explore novel biopolymer blends for improved mechanical properties, and integrate blockchain for enhanced traceability and transparency in waste valorization. A comprehensive understanding of the history of the development of the field and the issues it has not solved is important in accelerating the implementation of 3D printing in sustainable food waste management. This study concludes that 3D printing is a transformative approach to reducing food waste and advancing sustainability in the food and packaging sectors.

Nkosikho Dlangamandla, Johnson Zininga, Dmitrii Osipov et al.

This study investigated the impact of a two-stage pre-treatment process of sugarcane bagasse (SB) and aspen wood (AW) biomass to produce biobutanol. Mild acid and organosolv pre-treatment were combined to pre-treat the biomass and recover lignin. The pre-treated biomass was used for enzymatic saccharification. The total reducing sugars yield per 100 g of pre-treated biomass was 29.29 g/L (13.8 g/L glucose) and 27.79 g/L (12.45 g/L glucose) from AW and SB at 150 °C and 160 °C respectively. Both samples gave a lignin recovery of 15 %. The highest phenolic content obtained from the hydrolysates was 6.55 and 4.53 mg/L for SB and AW at 170 °C and 160 °C, respectively. The highest biobutanol concentration obtained was 7.08 and 8.65 g/L for SB and AW respectively after 72 h. Therefore, the two-stage pre-treatment process was successfully applied to obtain solid residues rich in cellulose as well as lignin, that could be fermented to produce biobutanol.

Mahdiyeh Bakhtiyari-Ramezani, Fatemeh Mohammadi, Maryam Azizi et al.

Landfill leachate poses a significant environmental threat. This study examined Non-thermal plasma (NTP) technology for treating landfill leachate after pretreatments to tackle this issue. EC and TDS decreased by over 96 %, COD by 98 %, turbidity by 97 %, color by 93 %, TSS by 98 %, and BOD5 by 97 %. Phenol levels dropped by more than 98 %, and significant heavy metal removal was achieved: Cd, Hg, Pb, Cu, Ni, Al, Fe, and Zn concentrations were reduced by 90 %, 95 %, 97 %, 92 %, 94 %, 72 %, 65 %, and 77 %, respectively. These findings underscore the potential of NTP technology as a valuable tool for wastewater treatment.

Prince O. Ukaogo, Okenwa U. Igwe, Ogechi C. Nwankwo et al.

This study evaluated the presence of polycyclic aromatic hydrocarbons (PAHs) and trace metals in Vernonia amygdalina and Ocimum basilicum leaves grown near Ekeonunwa, Relief, and Toronto industrial markets in Owerri. High-performance liquid chromatography (HPLC) and Inductively Coupled Plasma Optical Emission Spectrophotometry (ICP-OES) were employed for analysis, with Cold Vapour Atomic Fluorescence Spectrophotometry (CV-AFS) specifically for mercury detection. PAH Concentrations (mg kg−1 PAHs): V. amygdalina: Ekeonunwa (5.56), Relief (8.99), Toronto (0.13) O. basilicum: Ekeonunwa (7.18), Relief (3.37), Toronto (0.17), while The average levels of metals in the soil samples ranked in descending order as follows: Fe > Mn > Zn > Cu > Al > Cd > Pb > Cr > Co > V > Li > Hg, while those in the vegetable samples followed the sequence: Fe > Mn > Zn > Cu > Al > Pb > Cd > Cr > V > Co > Li > Hg. Average metal concentrations were higher than FAO/WHO maximum permissible limits. Estimated Daily Intake (EDI) values for all metals were lower than their respective Reference Doses (RfD), Health Risk Index (HRI), Target Hazard Quotient (THQ), and Hazard Index (HI) values for both vegetables were significantly below 1, suggesting minimal risk from metal exposure. However, Target Cancer Risk (TCR) and Cumulative Target Cancer Risk (CTCR) assessments indicated a potential elevated cancer risk for individuals consuming these vegetables from areas where risk thresholds were surpassed. Preventative measures are recommended in these specific locations.

Sony Kumari, Rahel Debbarma, Munquad Habibi et al.

Bananas (Musa paradisiaca) are among the most important tropical and subtropical crops, playing a vital role in global nutrition, food security, and regional economies. However, their large-scale cultivation generates substantial biomass waste-including rhizomes, pseudostems, leaves, rachis, fruit-bunch-stems, and peels-which remains underutilized. This review addresses the growing research need to valorize banana agro-waste, particularly green bananas and peels, for the development of biodegradable biofilms as sustainable alternatives to plastic packaging. The novelty of this work lies in its focused examination of banana-derived biopolymers, such as starch and fibre, and their capacity to form eco-friendly, mechanically robust, and biodegradable films suitable for food preservation. In addition to packaging applications, the review explores the broader multifunctionality of banana plant components across textiles, medicine, and bio-based industries. By synthesizing current literature, this article presents a comprehensive overview of banana waste utilization for both economic and environmental sustainability. It also identifies existing research gaps and outlines future directions to optimize biofilm formulations and encourage holistic, waste-minimizing approaches in banana production. Ultimately, this review highlights the untapped potential of banana waste as a valuable resource for advancing circular bioeconomy and sustainable innovation.

Analyse Villanueva Gaete, Ana Paula Martinazzo, Carlos Eduardo de Souza Teodoro

Currently, within the concepts of Bioeconomy and Circular Economy, technologies for transforming agro-indutrial waste into high-value products are rapidly advancing and gaining attention. Among these, microbial enzymes like cellulase have become valuable due to their extensive industrial application and potential to reduce cultivation cost, especially by using agro-industrial residues as substrates. This study aimed to optimize the culture conditions of Bacillus smithii QT03 for cellulase production and to partially characterize the enzyme. Several parameters were evaluated, including different agro-industrial wastes as a carbon source, incubation temperature, pH, incubation time, agitation, inoculum size, nitrogen source, and cellulase thermal and pH stability. B. smithii QT03 produces cellulase in the presence of malt bagasse as a carbon source (2 %), meat extract as a nitrogen source (1.5 %), at an incubation time of 48 h at 35 °C, stirring at 200 rpm, and 2 % inoculum. The enzyme reached maximum activity after 30 min at pH 7 and maintained 70 % of its activity for 24 h at pH 6. It also reached maximum activity at 70 °C and maintained 100 % activity after incubation at 55 °C for 3 h, and 58 % at 60 °C for 24 h. These results suggest that cellulase production by B. smithii QT03 using malt bagasse presents a promising, sustainable alternative for industrial applications.

Giacomo Magnifico, Eduard Barbu

This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-based BRIO summarization methods to the Newsela corpus, comparing outputs with manually annotated simplifications and achieving a top ROUGE-L score of 0.654. This provides insight into where summarization and simplification outputs converge and differ.

Jiaxu Wan, Xu Wang, Mengwei Xie et al.

Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.

Diogo J. Paulo, João Martins, Hugo Proença et al.

Urban waste management remains a critical challenge for the development of smart cities. Despite the growing number of litter detection datasets, the problem of monitoring overflowing waste containers, particularly from images captured by garbage trucks, has received little attention. While existing datasets are valuable, they often lack annotations for specific container tracking or are captured in static, decontextualized environments, limiting their utility for real-world logistics. To address this gap, we present StreetView-Waste, a comprehensive dataset of urban scenes featuring litter and waste containers. The dataset supports three key evaluation tasks: (1) waste container detection, (2) waste container tracking, and (3) waste overflow segmentation. Alongside the dataset, we provide baselines for each task by benchmarking state-of-the-art models in object detection, tracking, and segmentation. Additionally, we enhance baseline performance by proposing two complementary strategies: a heuristic-based method for improved waste container tracking and a model-agnostic framework that leverages geometric priors to refine litter segmentation. Our experimental results show that while fine-tuned object detectors achieve reasonable performance in detecting waste containers, baseline tracking methods struggle to accurately estimate their number; however, our proposed heuristics reduce the mean absolute counting error by 79.6%. Similarly, while segmenting amorphous litter is challenging, our geometry-aware strategy improves segmentation mAP@0.5 by 27% on lightweight models, demonstrating the value of multimodal inputs for this task. Ultimately, StreetView-Waste provides a challenging benchmark to encourage research into real-world perception systems for urban waste management.

Prakriti Tripathi

Industry partners provided a problem statement that involves classifying electronic waste using machine learning models that will be used by pick-and-place robots for waste segregation. This was achieved by taking common electronic waste items, such as a mouse and charger, unsoldering them, and taking pictures to create a custom dataset. Then state-of-the art YOLOv11 model was trained and run to achieve 70 mAP in real-time. Mask-RCNN model was also trained and achieved 41 mAP. The model can be integrated with pick-and-place robots to perform segregation of e-waste.