Hasil untuk "Standardization. Simplification. Waste"

A. Zorpas

There are numerous unresolved research questions, along with ongoing debates regarding how to achieve circular economy and at what level. The forthcoming circular economy standard (ISO 59000 framework) as a result from the ISO/TC 323, from the International Organization for Standardization (ISO) aims to offer global implementation pathways using a unified technical language. The most challenging aspect of circularity, whether viewed scientifically, technically, and/or legislatively, is how to enhance prosperity while reducing reliance on primary materials and energy to achieve climate neutrality by 2050, thereby aiding the EU in achieving a successful and equitable transition towards a sustainable future. Strategies in the framework of waste management and circular economy are essential and needed to reduce the impact of several processes on the environment through product, processes, and corporate policies using green applicable sustainable resources and environmental management systems. In addition, "measuring something that is not there" is very complex and not fully comprehensible, not clear and not tangible from organizations, researchers, policy makers and citizens. The willingness and ability of individuals or organizations to take actions towards a low-carbon society involves grappling with various perspectives, such as social norms and economic viability. Circular economy is considered a tool in combating climate change and implementing climate mitigation (as well as adaptation) measures. Moreover, to date, there has been no common scientific or technical language for the application of the circular economy concept. This paper highlights the multitude of "Rs" beyond the well-known (3Rs) Reduce-Reuse-Recycle pattern, which can be applied in various contexts to assist SMEs and organizations (and even more citizens) in successfully adopting circular economy principles, while also shedding light on how these "Rs" can be utilized to measure intangible aspects (something that is not there). The results indicates that more than 55Rs exist which directly involved in the circular economy framework considering also waste management strategies. The findings of this study reveal the existence of over 82 "Rs" beyond the well-known principles of "reduce, reuse, recycle," each playing a distinct role in the development of strategies aimed at addressing waste management issues and advancing circularity towards a low-carbon society. Furthermore, the results could be useful for any policy makers, consultants, engineers, practitioners, urban planners, academics etc., in order to develop, apply, monitor and improve any strategy such as waste prevention, reuse, reduce, energy recovery etc., in the framework of circular economy principles, solid waste management and beyond.

AbdulRahman Salem, Faisal Mustafa, Basil M. Darras et al.

The growing accumulation of end-of-life (EOL) electric vehicle (EV) batteries poses significant environmental challenges, despite EVs contributing to the global Sustainable Development Goals (SDGs). Improper recycling and disposal can cause eutrophication from lithium battery leaching and release harmful pollutants, such as toxic dioxins from lead-acid pyrolysis. This study develops a much-needed comprehensive lifecycle management framework designed to extend EV battery usability and reduce premature recycling impacts. The framework is guided by four key performance indicators (KPIs): state of health (SOH), state of safety (SOS), remaining nominal capacity, and battery chemistry. These KPIs support optimal allocation of retired batteries to second-life applications, including battery energy storage systems (BESS) for variable renewable energy systems (VRES), refurbishment, or recycling. The framework introduces a QR code-based battery passport system that enables real-time diagnostics and supports informed decision-making while ensuring compliance with government recycling regulations. To evaluate performance, MATLAB simulations were conducted and compared with standard machine learning models trained on the same KPIs. Results demonstrate that the proposed framework achieves higher classification effectiveness (98%) and superior environmental effectiveness than most benchmarked models (91.04%), while also incurring the fewest classification errors (1) compared to contemporary models. The findings highlight the potential of structured lifecycle management to mitigate environmental risks, optimize material recovery, and support the sustainable growth of EV adoption.

Jisha J., K. Martina Rani, Aldrin Joseph et al.

Despite increasing global concern for environmental sustainability, the adoption of recycled products remains limited, particularly in developing economies where consumer skepticism and entrenched purchasing habits persist. This study examines the key determinants influencing consumers’ intentions to purchase recycled products in alignment with Sustainable Development Goal 12 (Responsible Consumption and Production). Specifically, it analyzes the effects of environmental attitude, trust, perceived credibility, and perceived product quality on purchasing decisions. A structured questionnaire survey was administered to 315 urban consumers in India using purposive sampling. The findings reveal that environmental attitude is the most influential predictor of recycled product purchase intention, indicating that environmentally conscious consumers are more likely to engage in sustainable consumption. Trust and perceived credibility also significantly affect purchasing behavior, emphasizing the importance of authenticity and reputation in fostering acceptance of recycled products. Although perceived product quality has a comparatively weaker influence, it remains relevant in meeting expectations related to usability and design. The study contributes theoretically by integrating psychological, perceptual, and normative factors into an SDG 12–oriented behavioral framework. Practically, it provides insights for marketers and policymakers to enhance communication strategies, build consumer trust, and promote circular economy practices.

Waseem Ahmed, Yujuan Li, Edward Mwando et al.

Antoni Sánchez

Farnaz Fotovvatikhah, Ismail Ahmedy, R. M. Noor et al.

Waste classification is a critical step in waste management that is time-consuming and necessitates automation to replace traditional approaches. Recently, machine learning (ML) and deep learning (DL) have gained attention from researchers seeking to automate waste classification by providing alternative computational techniques to address various waste-related challenges. Significant research on waste classification has emerged in recent years, reflecting the growing focus on this domain. This systematic literature review (SLR) explores the role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in automating waste classification. Using Kitchenham’s and PRISMA guidelines, we analyze over 97 studies, categorizing AI-based techniques into ML-based, DL-based, and hybrid models. We further present an in-depth review of over fifteen publicly available waste classification datasets, highlighting key limitations such as dataset imbalance, real-world variability, and standardization issues. Our analysis reveals that deep learning and hybrid approaches dominate the current research landscape, with CNN-based architecture and transfer learning techniques showing particularly promising results. To guide future advancements, this study also proposes a structured roadmap that organizes challenges and opportunities into short-, mid-, and long-term priorities. The roadmap integrates insights on model accuracy, system efficiency, and sustainability goals to support the practical deployment of AI-powered waste classification systems. This work provides researchers with a comprehensive understanding of the state-of-the-art in ML and DL for waste classification and offers insights into areas that remain unexplored.

Joong-Ku Lee, Hyeonseok Choi, Young Soo Park et al.

This paper presents a Learning from Teleoperation (LfT) framework that integrates human expertise with robotic precision to enable robots to autonomously perform skills learned from human operators. The proposed framework addresses challenges in nuclear waste handling tasks, which often involve repetitive and meticulous manipulation operations. By capturing operator movements and manipulation forces during teleoperation, the framework utilizes this data to train machine learning models capable of replicating and generalizing human skills. We validate the effectiveness of the LfT framework through its application to a power plug insertion task, selected as a representative scenario that is repetitive yet requires precise trajectory and force control. Experimental results highlight significant improvements in task efficiency, while reducing reliance on continuous operator involvement.

Timo Lange, Ajish Babu, Philipp Meyer et al.

Current disposal facilities for coarse-grained waste perform manual sorting of materials with heavy machinery. Large quantities of recyclable materials are lost to coarse waste, so more effective sorting processes must be developed to recover them. Two key aspects to automate the sorting process are object detection with material classification in mixed piles of waste, and autonomous control of hydraulic machinery. Because most objects in those accumulations of waste are damaged or destroyed, object detection alone is not feasible in the majority of cases. To address these challenges, we propose a classification of materials with multispectral images of ultraviolet (UV), visual (VIS), near infrared (NIR), and short-wave infrared (SWIR) spectrums. Solution for autonomous control of hydraulic heavy machines for sorting of bulky waste is being investigated using cost-effective cameras and artificial intelligence-based controllers.

Federico Gibellini, Piero Fraternali, Giacomo Boracchi et al.

Improper solid waste management represents both a serious threat to ecosystem health and a significant source of revenues for criminal organizations perpetrating environmental crimes. This issue can be mitigated thanks to the increasing availability of Very-High-Resolution Remote Sensing (VHR RS) images. Modern image-analysis tools support automated photo-interpretation and large territory scanning in search of illegal waste disposal sites. This paper illustrates a semi-automatic waste detection pipeline, developed in collaboration with a regional environmental protection agency, for detecting candidate illegal dumping sites in VHR RS images. To optimize the effectiveness of the waste detector at the core of the pipeline, extensive experiments evaluate such design choices as the network architecture, the ground resolution and geographic span of the input images, as well as the pretraining procedures. The best model attains remarkable performance, achieving 92.02 % F1-Score and 94.56 % Accuracy. A generalization study assesses the performance variation when the detector processes images from various territories substantially different from the one used during training, incurring only a moderate performance loss, namely an average 5.1 % decrease in the F1-Score. Finally, an exercise in which expert photo-interpreters compare the effort required to scan large territories with and without support from the waste detector assesses the practical benefit of introducing a computer-aided image analysis tool in a professional environmental protection agency. Results show that a reduction of up to 30 % of the time spent for waste site detection can be attained.

Krishna Chaitanya Marturi, Heba H. Elwazzan

In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employs large language models (LLMs) to first generate a structured plan, followed by plan-driven simplification of individual sentences. At the document level, we leverage LLMs to produce concise summaries and subsequently guide the simplification process using these summaries. This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.

Krishna Chaitanya Marturi, Heba H. Elwazzan

In this paper, we describe our methodology for the CLEF 2025 SimpleText Task 2, which focuses on detecting and evaluating creative generation and information distortion in scientific text simplification. Our solution integrates multiple strategies: we construct an ensemble framework that leverages BERT-based classifier, semantic similarity measure, natural language inference model, and large language model (LLM) reasoning. These diverse signals are combined using meta-classifiers to enhance the robustness of spurious and distortion detection. Additionally, for grounded generation, we employ an LLM-based post-editing system that revises simplifications based on the original input texts.

Sonam Priyadarshani, Preeti Nandal, Anju Arora et al.

Actinobacteria belonging to genus Streptomyces are a versatile group actively involved in global C cycle with abilities to degrade several recalcitrant substrates. Inhabiting diverse ecological niches, they are active in different pH and temperature regimes thus a source of robust enzymes for exploitation in bioprocessing. Compost is one such habitat supporting huge microbial diversity, lignocellulolytic actinobacteria being predominant in community. In this study, two actinobacterial strains isolated from compost through enrichment culture, identified as Streptomyces thermoviolaceous S1 and S2, showed lignocellulolose degrading enzyme production. When grown on rice straw under solid state fermentation they disrupted lignocellulose matrix. Structural changes in solid substrate were observed by non-invasive techniques SEM, XRD and FTIR. Alkali extraction of fermented solids removed about ∼ 33 % lignin from rice straw while buffer extracts showed high specific activities of all three components of cellulases, xylanase (84 IU/ mL), laccase (59 IU/ mL) and lignin peroxidase (26 IU/ mL).S. thermoviolaceous S2 showed better enzyme activities, lignin removal and cellulose enrichment than S1 (53.03 % and 49.01 % by S. thermoviolaceous S2 and S1 respectively). Alkali extraction led to efficient lignin removal than buffer extraction as evident from higher absorbance of alkali extracts (@205 nm) which was corroborated by higher recovery of acid precipitable lignin. Better cellulose enrichment enabled higher glucan loading and higher sugar yields upon enzymatic saccharification than uninoculated substrate. This study outlined a green like process involving biological treatment of rice straw with S. thermoviolaceous strains for delignification, lignin recovery and simultaneous lignocellulose degrading enzyme production for biomass processing.

Mohan Bhandari, Ghanashyam Tiwari, Maheshwor Dhakal

Nepal’s growing urbanization has intensified the challenges of waste management, yet it also opens opportunities to transform waste into valuable resources. This study explores the “Waste to Wealth” paradigm, focusing on recycling and resource management for sustainable development in Nepal. The study aims to understand waste management practices, identify key challenges, and uncover opportunities for sustainable recycling and resource valorization across diverse urban contexts. Adopting an interpretivist philosophy and a qualitative approach, the research engages with stakeholders in Kathmandu, Pokhara, Butwal, and Dhangadi. Purposive sampling includes policymakers, municipal officers, community members, social entrepreneurs, and environmental activists. Semi-structured interviews conducted in Nepali yield rich insights, analyzed thematically using Braun & Clarke’s (Braun and Clarke, 2006) six-phase framework. Ethical standards, peer debriefing, and meticulous documentation ensure rigor and credibility. The findings are categorized into key themes as waste composition and current practices (organic waste dominates, with limited segregation at source), role of the informal sector in collection and recycling, resource valorization to create economic opportunities, socio economic impacts, innovative practices and challenges (weak municipal services, inadequate infrastructure, limited community awareness, and fragmented policy enforcement hinder progress). Turning waste into wealth in Nepal demands coordinated efforts among policymakers, communities, and entrepreneurs. With strategic support, localized innovations, and inclusive governance, waste can become a catalyst for sustainable development.

Pamon Pumas, Maliwan Puangmanee, Pimpawat Teeratitayangkul et al.

This study examines the behavioral and social factors influencing household waste separation practices in Keelek Subdistrict Municipality, Chiang Mai Province, Thailand. Drawing on survey data and a mixed‐methods approach that integrates correlation analysis, principal component analysis, and a two‐stage machine‐learning pipeline—further validated by confirmatory structural equation modeling of Attitude → Intention → Behavior and mapped onto an established nudge taxonomy—the research identifies the most influential predictors of separation behavior. These include routine organic waste sorting, behavioral intention, emotional commitment, and the perceived influence of community members and local authorities. Among the tested models, Gradient Boosting Regression yielded the highest predictive accuracy (R2 = 0.782; MAE = 0.331), underscoring its ability to capture complex non-linear behavioral patterns more effectively than traditional approaches. By uniting behavioral theory, community-derived insights, and predictive analytics, this work advances a novel, transferable framework for municipal planning. It offers practical, ESG/SDG–aligned strategies—such as habit-based, peer-supported nudges and AI-powered monitoring systems—that local governments can adopt to design evidence-based waste policies. Focusing on a semi-urban context often overlooked in the literature, this study fills a critical methodological gap and charts a replicable pathway for scaling behaviorally informed waste-management interventions.

Kanwal, Okezie Emmanuel, Rozina et al.

Addressing the dual challenges of greenhouse gas emissions and fossil fuel depletion requires sustainable and cost-effective energy solutions. This study investigates biodiesel production from non-edible Calotropis gigantea L. seed oil using a novel copper oxide (CuO) nano-catalyst synthesized from the green pods of C. gigantea. CuO nanoparticles were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). Optimal biodiesel production conditions were achieved at a methanol-to-oil molar ratio of 9:1, reaction temperature of 80 °C, reaction time of 105 min, and catalyst loading of 0.74 wt%, resulting in a 90 % yield. The synthesized biodiesel was characterized through FT-IR spectroscopy, and gas chromatography-mass spectrometry (GC–MS). Physicochemical analysis demonstrated compliance with both European (EN 14214) and American (ASTM D 6751) biodiesel standards, exhibiting favorable properties including density (0.792 kg/L), acid value (0.34 mg KOH/g), kinematic viscosity (6 mm2/s), flash point (91 °C), cloud point (−10 °C), pour point (−8 °C), and minimal sulphur content (0.00097 wt%). These findings establish the viability of converting toxic, non-edible C. gigantea seeds into high-quality biodiesel, presenting a promising pathway toward sustainable energy production while potentially fostering regional socioeconomic development through valorization of agricultural waste.

Pranav Prashant Dagwar, Syed Suffia Iqbal, Deblina Dutta

Rare earth elements (REEs) play a vital role in digitalization and industrialization. Naturally occurring in bastnasite, monazite, and xenotime, REEs are primarily concentrated in China, Australia, and the USA, leading to dependence on secondary sources. Recycling REEs from industrial waste such as E-waste, wastewater, red mud, slag, and fly ash offers a sustainable, low-emission, and energy-efficient solution. Advanced methods, including bio-metallurgy, have optimized recovery, achieving 80–95% efficiency for elements like Yttrium, Cerium, Neodymium, and Thorium. However, improper handling of secondary REE resources poses environmental and health risks. This study comprehensively explores REEs’ role in sustainable industrial growth, evaluating traditional and advanced recycling technologies. It also assesses the ecotoxicological impacts of REEs and emphasizes safety measures. Additionally, the review highlights circular economy strategies for sustainable development, addressing environmental challenges while promoting efficient resource utilization.

Priyam Saxena, Xing Song, Baiyu Zhang et al.

This study investigates the influence of landfill management practices on the release of polybrominated diphenyl ethers (PBDEs) from coastal landfills in Newfoundland, Canada. By comparing PBDE congener profiles in leachate from a modern landfill with advanced treatment systems and a legacy landfill with limited infrastructure, we demonstrate the critical role of modern waste management practices in mitigating PBDE contamination. Both sites showed PBDE contamination, but the legacy landfill exhibited greater variability in congener types and concentrations. BDE-47 emerged as the predominant congener at both sites, with episodic spikes at the legacy landfill reaching 14.39 ng/L, alongside the presence of congeners like BDE-77, BDE-100, and BDE-183. GIS analysis revealed PBDE dispersion into nearby surface waters, posing risks to marine ecosystems. Landfill operator surveys provided insights into operational challenges, including limited e-waste diversion, fire risks from batteries, and inadequate leachate treatment at the legacy site, contributing to its vulnerability. This study underscores the need for proactive PBDE management in coastal landfills. The adoption of modern landfill technologies and enhanced e-waste diversion programs is vital for reducing contamination and protecting marine environments. These findings highlight the importance of sustainable waste management practices in safeguarding coastal ecosystems.

Yuichi Ishimura, Kai Nomura, Daisuke Ichinose

This study explores the effects of a policy intervention designed to simplify the standards for plastic waste separation on collection volume and the quality of recyclables. We employ a causal impact analysis based on a Bayesian structural time-series approach to estimate the effects of simplifying the municipal solid waste-separation process for plastic waste in Japan. We find that simplifying plastic waste-separation standards increases plastic packaging waste-collection volume. This effect seems to be largely driven by behavioral changes such as decreased time spent on waste separation. We also find that simplifying home separation increases the percentage of contaminated plastic packaging waste collected for recycling and other materials not subject to collection in the post-collection period. Several robustness and falsification tests corroborated these results. Our results highlight the importance of considering the trade-off between the quantity and quality of recyclables when designing plastic waste recycling policies.

Diego Rossit, Jonathan Bard

An efficient Municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and livability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, e.g., the waste generation rate in an urban area. With this in mind, this paper contributes with a robust optimization model to design the network of collection points (i.e., location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

Joong-Ku Lee, Young Soo Park

This paper introduces a dual-arm telerobotic platform designed to efficiently and safely execute hot cell operations for nuclear waste disposition at EM sites. The proposed system consists of a remote robot arm platform and a teleoperator station, both integrated with a software architecture to control the entire system. The dual-arm configuration of the remote platform enhances versatility and task performance in complex and hazardous environments, ensuring precise manipulation and effective handling of nuclear waste materials. The integration of a teleoperator station enables human teleoperator to remotely control the entire system real-time, enhancing decision-making capabilities, situational awareness, and dexterity. The control software plays a crucial role in our system, providing a robust and intuitive interface for the teleoperator. Test operation results demonstrate the system's effectiveness in operating as a remote hotbox for nuclear waste disposition, showcasing its potential applicability in real EM sites.