Waste management最新文献_第10页

Bacterial biofilm-based bioleaching: Sustainable mitigation and potential management of e-waste pollution 基于细菌生物膜的生物浸出：电子废物污染的可持续缓解和潜在管理。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.010

Krishnamurthy Mathivanan , Ruiyong Zhang , Jayaraman Uthaya Chandirika , Thangavel Mathimani , Can Wang , Jizhou Duan

Significant advances in the electrical and electronic industries have increased the use of electrical and electronic equipment and its environmental emissions. The e-waste landfill disposal has deleterious consequences on human health and environmental sustainability, either directly or indirectly. E-waste containing ferrous and non-ferrous materials can harm the surrounding aquatic and terrestrial environments. Therefore, recycling e-waste and recovering metals from it before landfill disposal is an important part of environmental management. Although various chemical and physical processes are being used predominantly to recover metals from e-waste, the bioleaching process has gained popularity in recent years due to its eco-friendliness and cost-effectiveness. Direct contact between microbes and e-waste is crucial for continuous metal dissolution in the bio-leaching process. Biofilm formation is key for the continuous dissolution of metals from e-waste in contact bioleaching. Critical reviews on microbial activities and their interaction mechanisms on e-waste during metal bioleaching are scarce. Therefore, this review aims to explore the advantages and disadvantages of biofilm formation in contact bioleaching and the practical challenges in regulating them. In this review, sources of e-waste, available metallurgical methods, bioleaching process, and types of bioleaching microbes are summarized. In addition, the significance of biofilm formation in contact bioleaching and the role and correlation between EPS production, cyanide production, and quorum sensing in the biofilm are discussed for continuous metal dissolution. The review reveals that regulation of quorum sensing by exogenous and endogenous processes facilitates biofilm formation, leading to continuous metal dissolution in contact bioleaching.

电气和电子工业的重大进步增加了电气和电子设备的使用及其对环境的排放。电子垃圾填埋处理直接或间接地对人类健康和环境可持续性造成有害后果。含有铁和有色金属材料的电子垃圾会损害周围的水生和陆地环境。因此，在填埋前回收电子垃圾并从中回收金属是环境管理的重要组成部分。虽然各种化学和物理过程主要用于从电子废物中回收金属，但生物浸出过程近年来因其环保和成本效益而受到欢迎。在生物浸出过程中，微生物与电子垃圾的直接接触对金属的连续溶解至关重要。生物膜的形成是接触生物浸出中电子垃圾中金属连续溶解的关键。金属生物浸出过程中电子垃圾中微生物的活性及其相互作用机制的评述很少。因此，本文旨在探讨接触式生物浸出中生物膜形成的优缺点及其调控的实际挑战。本文综述了电子垃圾的来源、现有的冶金方法、生物浸出工艺以及生物浸出微生物的种类。探讨了接触浸出过程中生物膜形成的意义，以及生物膜中EPS生成、氰化物生成和群体感应在金属连续溶出过程中的作用和相互关系。研究表明，在接触浸出过程中，外源和内源过程对群体感应的调节促进了生物膜的形成，从而导致金属的持续溶解。

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引用次数: 0

ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification eccn - net：一种基于深度学习的高效有机和可回收废物分类技术。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.023

Md. Sakib Bin Islam , Md. Shaheenur Islam Sumon , Molla E. Majid , Saad Bin Abul Kashem , Mohammad Nashbat , Azad Ashraf , Amith Khandakar , Ali K. Ansaruddin Kunju , Mazhar Hasan-Zia , Muhammad E.H. Chowdhury

Efficient waste management is essential to minimizing environmental harm as well as encouraging sustainable progress. The escalating volume and sophistication of waste present significant challenges, prompting innovative methods for effective waste categorization and management. Deep learning models have become highly intriguing tools for automating trash categorization activities, providing effective ways to optimize processes for handling waste. Our work presents a novel deep learning method for trash classification, with the goal to improve the accuracy, also efficiency of garbage image categorization. We examined the effectiveness of several pre-trained models, such as InceptionV2, Densenet201, MobileNet v2, and Resnet18, using objective evaluation and cross-validation. We proposed an Eco Cycle Classifier Deep Neural Network (ECCDN-Net) model that is particularly built for the categorization of waste images. ECCDN-Net utilizes the advantageous qualities of Densenet201 and Resnet18 by merging their capacities to extract features, enhanced with auxiliary outputs to optimize the classification procedure. The set of images used in this study comprises 24,705 images that are divided into two distinct classes: Organic and Recyclable. The set allows extensive evaluation and training of deep learning models for waste classification of images tasks. Our research demonstrates that the ECCDN-Net model classifies waste images with 96.10% accuracy, outperforming other pre-trained models. Resnet18 had 92.68% accuracy, MobileNet v2 93.27%, Inception v3 94.77%, and Densenet201, a significant improvement, 95.98%. ECCDN-Net outperformed these models in waste image categorization with 96.10% accuracy. We ensure the reliability and generalizability of our methods throughout the dataset by integrating and cross-validating deep learning models. The current work introduces an innovative deep learning-based approach that has promising potential for waste categorization and management strategies.

有效的废物管理对于尽量减少对环境的危害和鼓励可持续发展至关重要。废物的数量和复杂性不断增加，提出了重大挑战，促使采用创新方法进行有效的废物分类和管理。深度学习模型已经成为自动化垃圾分类活动的非常有趣的工具，为优化垃圾处理过程提供了有效的方法。本文提出了一种新的深度学习垃圾分类方法，旨在提高垃圾图像分类的准确率和效率。通过客观评估和交叉验证，我们检验了几种预训练模型的有效性，如InceptionV2、Densenet201、MobileNet v2和Resnet18。我们提出了一个生态循环分类器深度神经网络（ECCDN-Net）模型，该模型是专门为垃圾图像分类而建立的。ECCDN-Net利用了Densenet201和Resnet18的优势，通过合并它们提取特征的能力，并辅以辅助输出来优化分类过程。本研究中使用的图像集包括24,705张图像，分为两个不同的类别：有机和可回收。该集允许广泛的评估和训练深度学习模型，用于图像任务的废物分类。我们的研究表明，ECCDN-Net模型对垃圾图像的分类准确率为96.10%，优于其他预训练模型。Resnet18的准确率为92.68%，MobileNet v2的准确率为93.27%，Inception v3的准确率为94.77%，Densenet201的准确率为95.98%。ECCDN-Net在垃圾图像分类方面优于这些模型，准确率为96.10%。我们通过整合和交叉验证深度学习模型来确保我们的方法在整个数据集中的可靠性和泛化性。目前的工作介绍了一种创新的基于深度学习的方法，该方法在废物分类和管理策略方面具有很大的潜力。

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引用次数: 0

Unveiling the resource potential of space debris: A forecast of valuable metals to 2050 揭示空间碎片的资源潜力：对2050年贵重金属的预测。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.019

Fumihiro Hayashi , Arata Kioka , Takuma Ishii , Takumu Nakamura

The proliferation of space debris poses a significant challenge in modern space exploration, with potential repercussions for the future space environment and activities. Various research and technological developments have addressed these concerns, including estimating the number of space debris orbiting the Earth and its efficient removal. This paper proposes a novel resource-oriented perspective on space debris and focuses on the composition and resource potential of space debris. This study forecasts for the first time the annual mass changes in resource materials (Al, Al₂O₃, Ti, Fe, Cu, and Ag) by the year 2050 by employing a debris environment model simulation. Our simulation reveals that the masses of all the studied resource elements in an Earth orbital altitude of 400 km will increase by 2050. For example, Al and Ti at the 400 km altitude band will increase from 3.0 × 10⁶ kg and 3.2 × 10⁵ kg (in 2016) to 3.8 × 10⁷ kg and 4.2 × 10⁶ kg (in 2050), respectively, climbing at least ten times from 2016 to 2050, on the conservative estimates with a high post-mission disposal success rate. These comparative influxes of Al and Ti in 2050 due to space debris are at least 100 times higher than the natural influxes into the Earth’s atmosphere due to meteoroids, further highlighting the significance of space debris. Our simulation results suggest that space debris may hold significant space resource potential in the next 25 years but can be a considerable environmental contaminant impeding space sustainability.

空间碎片的扩散对现代空间探索构成重大挑战，并可能对未来的空间环境和活动产生影响。各种研究和技术发展已经解决了这些问题，包括估计绕地球轨道运行的空间碎片的数量及其有效清除。本文提出了一种新的空间碎片资源导向视角，重点研究了空间碎片的组成和资源潜力。本文首次采用岩屑环境模型模拟，预测了到2050年资源材料（Al、Al2O3、Ti、Fe、Cu和Ag）的年质量变化。我们的模拟表明，到2050年，在地球轨道高度400公里处，所有研究资源元素的质量都将增加。例如，400 km高度波段的Al和Ti将分别从3.0 × 106 kg和3.2 × 105 kg（2016年）增加到3.8 × 107 kg和4.2 × 106 kg（2050年），保守估计从2016年到2050年至少增加10倍，任务后处置成功率很高。2050年空间碎片造成的Al和Ti的相对流入至少是流星体自然流入地球大气层的100倍，进一步凸显了空间碎片的重要性。我们的模拟结果表明，空间碎片在未来25年内可能具有巨大的空间资源潜力，但也可能成为阻碍空间可持续性的重大环境污染物。

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引用次数: 0

Hydrothermal pretreatment for enhanced thermochemical or biochemical conversion of pharmaceutical biowastes into fuels, fertilizers, and carbon materials 通过水热预处理，加强制药生物废料向燃料、肥料和碳材料的热化学或生物化学转化。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.007

Yilin Wei , Xiang Meng , Weiyuan Meng , Lijian Leng , Zhiyong Zeng , Xinming Wang , Shengqiang Liu , Hao Zhan

Pharmaceutical biowastes, rich in organic matter and high in moisture, are typical light industry byproducts with waste and renewable attributes. Thermochemical and biochemical conversion technologies transform these residues into value-added bioproducts, including biofuels, biofertilizers, and bio-carbon materials. Hydrothermal pretreatment effectively removes toxic substances and enhances feedstock for these processes. This review comprehensively examines its role in improving the formation of bioproducts from pharmaceutical biowastes, focusing on (i) upgrading and denitrogenating solid biofuels with better combustion performance; (ii) enhancing biodegradability and gaseous biofuel production via organic matter decomposition; (iii) enriching soluble carbon and nitrogen for liquid biofertilizer; (iv) eliminating antibiotic residues and reducing antibiotic resistance in solid biofertilizers; and (v) stabilizing carbon and nitrogen structures and optimizing pore characteristics for functionalized carbon materials. The review recommends a potential staged thermochemical approach to co-produce nitrogen-enriched liquid biofertilizers and porous carbon materials from pharmaceutical biowastes. Hydrothermal pretreatment emerges as a key technique for facilitating the migration and conversion of essential elements like carbon and nitrogen. This study reveals the potential of hydrothermal pretreatment to address the limitations of pharmaceutical biowastes and offers insights into their valorization.

医药生物废弃物是典型的轻工业副产物，具有废弃物和可再生特性，有机质含量高，水分含量高。热化学和生物化学转化技术将这些残留物转化为增值生物产品，包括生物燃料、生物肥料和生物碳材料。水热预处理有效地去除了有毒物质，提高了这些工艺的进料质量。本文综述了其在改善制药生物废弃物形成生物产品方面的作用，重点介绍了(1)具有更好燃烧性能的固体生物燃料的升级和脱氮；（ii）通过有机物分解提高生物可降解性和气体生物燃料的生产；（三）为液体生物肥料富集可溶性碳和氮；（iv）消除固体生物肥料中的抗生素残留并降低抗生素耐药性；(5)稳定功能化碳材料的碳氮结构和优化孔隙特性。该综述推荐了一种潜在的分阶段热化学方法，可以从制药生物废弃物中共同生产富氮液体生物肥料和多孔碳材料。水热预处理是促进碳、氮等必需元素迁移转化的关键技术。本研究揭示了水热预处理解决制药生物废物局限性的潜力，并为其增值提供了见解。

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引用次数: 0

Perceptions of value in retired smartphones: The role of monetary incentives in influencing end-of-hibernation decisions 对退休智能手机价值的看法：货币激励在影响报废决定中的作用。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.015

Mostafa Sabbaghi

Each year, a significant number of smartphones are retired, yet retained by consumers. These hibernating smartphones have the reuse potential for another lifecycle. Nonetheless, they often stay in storage for a long time and may ultimately face inadequate recovery. This study explores consumers’ perceptions regarding the value of hibernating smartphones over time. It examines the influence of factors such as the duration of smartphone storage and monetary incentives on users’ decision-making regarding the End-of-Hibernation (EoH). The findings demonstrate that, on average, participants perceive the value of a newly retired smartphone to be 28% higher than its market value. This perceived value increases to 83% after three years since the smartphone’s retirement. Participants’ tendency to keep a hibernating smartphone increases as the gap between the monetary incentive and their perceived value of the device increases. Conversely, the longer the smartphone stays in hibernation, the less inclined users are to keep it.

每年都有大量智能手机退役，但仍被消费者保留。这些 "冬眠 "的智能手机具有在另一个生命周期重复使用的潜力。然而，它们往往会被长期存放，最终可能面临回收不足的问题。本研究探讨了消费者对冬眠智能手机价值的看法。研究探讨了智能手机的存储时间和货币激励等因素对用户做出 "结束休眠"（EoH）决策的影响。研究结果表明，平均而言，参与者认为新退役智能手机的价值比其市场价值高 28%。智能手机退役三年后，这种感知价值增加到 83%。随着金钱奖励与参与者对智能手机感知价值之间差距的扩大，参与者保留冬眠智能手机的倾向也随之增加。相反，智能手机冬眠的时间越长，用户就越不愿意保留它。

引用次数: 0

Differentiating low-carbon waste management strategies for bio-based and biodegradable plastics under various energy decarbonization scenarios 不同能源脱碳情景下生物基塑料和可生物降解塑料的低碳废物管理策略的区别。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.001

Yuxin Huang , Mengqi Han , Zhujie Bi , Nannan Gu , Dungang Gu , Tingting Hu , Guanghui Li , Jiaqi Lu

Bio-based and biodegradable (bio-)plastics are heralded as a key solution to mitigate plastic pollution and reduce CO₂ emissions. Yet, their end-of-life treatments embodies complex energy and material interactions, potentially leading to emissions through incineration or recycling. This study investigates the cradle-to-grave, emphasizing the waste management stage, carbon footprint for several types of bio-plastics, leveraging both GWP100a and CO₂ uptake methods to explore the carbon reduction benefits of recycling over disposal. Our findings indicate that in scenarios characterized by carbon-intensive electricity, using polylactic acid (PLA) as an example, incineration with energy recovery (−1.6316 kg CO₂-eq/kg, PLA) yields a more favorable carbon footprint compared to chemical recycling (−1.5317 kg CO₂-eq/kg, PLA). In contrast, in environments with a high proportion of renewable energy, chemical recycling is a superior method, and compared to incineration (−1.4087 kg CO₂-eq/kg, PLA), the carbon footprint of chemical recycling (−2.0406 kg CO₂-eq/kg, PLA) are significantly reduced. While mechanical recycling presents considerable environmental benefits, its applicability is constrained by the waste quality, especially in the case of biodegradable plastics like PLA. In addition, the degradation of biodegradable plastics such as PLA was modeled during compost and anaerobic digestion processes. This enables us to quantify the specific biogenic carbon emissions released during these processing steps, revealing the direct emissions with dynamic degradation. This study highlights the importance of tailoring bio-plastic waste management strategies to support global energy decarbonization while understanding their life-cycle carbon metabolism to effectively tackle plastic pollution and climate change.

生物基和可生物降解（生物）塑料被誉为减轻塑料污染和减少二氧化碳排放的关键解决方案。然而，它们的报废处理体现了复杂的能量和材料相互作用，可能导致通过焚烧或回收排放。本研究考察了从摇篮到坟墓的过程，强调了废物管理阶段，几种生物塑料的碳足迹，利用GWP100a和二氧化碳吸收方法来探索回收比处置的碳减排效益。我们的研究结果表明，在以碳密集型电力为特征的情况下，以聚乳酸（PLA）为例，与化学回收（-1.5317 kg CO2-eq/kg， PLA）相比，焚烧与能量回收（-1.6316 kg CO2-eq/kg， PLA）产生更有利的碳足迹。相比之下，在可再生能源比例较高的环境中，化学回收是一种优越的方法，与焚烧（-1.4087 kg CO2-eq/kg， PLA）相比，化学回收的碳足迹（-2.0406 kg CO2-eq/kg， PLA）显著降低。虽然机械回收具有可观的环境效益，但其适用性受到废物质量的限制，特别是在PLA等可生物降解塑料的情况下。此外，在堆肥和厌氧消化过程中模拟了PLA等可生物降解塑料的降解。这使我们能够量化在这些加工步骤中释放的特定生物源碳排放，揭示动态降解的直接排放。本研究强调了定制生物塑料废物管理策略的重要性，以支持全球能源脱碳，同时了解其生命周期的碳代谢，以有效应对塑料污染和气候变化。

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引用次数: 0

An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions 一个可解释的机器学习系统，用于有效利用耐用混凝土中的废玻璃，以最大限度地提高碳信用额，实现净零排放。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.034

Xu Huang, Junhui Huang, Sakdirat Kaewunruen

Recycling waste glass (WG) can be time-consuming, costly, and impractical. However, its incorporation into concrete significantly reduces environmental impact and carbon emissions. This paper introduces machine learning (ML) to civil engineering to optimise WG utilisation in concrete, supporting sustainability objectives. By employing a dataset of 471 experimental samples of waste glass concrete (WGC), various ML algorithms are applied, including Gradient Boosting Regressor (GBR), Random Forest (RF), Support Vector Regression (SVR), Adaptive Boosting (AdaBoost), Deep Neural Network (DNN), and k-Nearest Neighbours (kNN), to predict properties containing compressive strength (CS), alkali-silica reaction (ASR), and saved carbon credits (SCC). The proposed models achieve outstanding prediction performance with Coefficient of determination (R²) values of 0.95 for CS, 0.97 for ASR, and 0.99 for SCC using GBR and SVR, demonstrating high prediction accuracy with Root mean square error (RMSE) values of 3.31 MPa for CS, 0.03 % for ASR, and 0.11 for SCC. The SHapley Additive exPlanations (SHAP) analysis is utilised to interpret the model results, ensuring transparency and interpretability of the proposed ML models. The results reveal that the incorporation level of WG is a more significant influencing factor for these properties than the mean size of WG (MSWG).

回收废玻璃（WG）既耗时又昂贵，而且不切实际。然而，将其掺入混凝土中可以显著减少对环境的影响和碳排放。本文将机器学习（ML）引入土木工程，以优化混凝土中的WG利用率，支持可持续发展目标。通过使用471个废玻璃混凝土（WGC）实验样本的数据集，应用各种ML算法，包括梯度增强回归器（GBR）、随机森林（RF）、支持向量回归（SVR）、自适应增强（AdaBoost）、深度神经网络（DNN）和k-近邻（kNN），来预测包含抗压强度（CS）、碱-硅反应（ASR）和节省碳信用额度（SCC）的性能。基于GBR和SVR的模型预测CS、ASR和SCC的决定系数（R2）分别为0.95、0.97和0.99，具有较高的预测精度，CS、ASR和SCC的均方根误差（RMSE）分别为3.31 MPa、0.03%和0.11。SHapley加性解释（SHAP）分析用于解释模型结果，确保提议的ML模型的透明度和可解释性。结果表明，与平均粒径（MSWG）相比，水泥浆掺入量对这些性能的影响更为显著。

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引用次数: 0

Advanced recycling and identification system for discarded capacitors utilizing laser-induced breakdown spectroscopy technology 利用激光诱导击穿光谱技术的先进废旧电容器回收识别系统。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.11.044

Wenhan Gao , Boyuan Han , Yanpeng Ye , Yuyao Cai , Jun Feng , Yihui Yan , Yuzhu Liu

In the modern electronics industry, with the rapid development of technology and the quick turnover of electronic products, the production of electronic waste (e-waste) has also dramatically increased. Among these, discarded capacitors are a significant component of e-waste. These old capacitors not only contain harmful chemicals but are also rich in economically recoverable precious metals like Nb and Ag. This study specifically aims to enhance the classification of discarded capacitors to enable more efficient recycling and resource recovery.Traditional methods of capacitor classification mainly rely on manual identification, which is inefficient and limited in accuracy. To enhance the efficiency and accuracy of classification, this study introduces, for the first time, the combination of Laser-Induced Breakdown Spectroscopy (LIBS) technology and machine learning for the classification of capacitors. The Backpropagation Artificial Neural Network (BP-ANN) algorithms can be trained to automatically identify and classify discarded capacitors. To achieve better performance, we developed a novel algorithm called the Optimized Feature Extraction Variance Algorithm (OFEVA), which addresses the limitations of existing methods by significantly improving the accuracy of the classification model. Compared to training with principal component scores data from traditional Principal Component Analysis (PCA), training with OFEVA achieves higher classification accuracy and computational efficiency.This innovative approach not only helps increase the recycling rate of discarded capacitors and reduce environmental pollution but also provides significant technical support for the reuse of resources, thereby making an important contribution to the fields of environmental protection and resource recycling. In addition, the spectral lines of pure niobium have been calibrated for the first time in this paper, providing important data for further spectroscopic studies.

在现代电子工业中，随着科技的飞速发展和电子产品的快速周转，电子垃圾（e-waste）的产生量也急剧增加。其中，废弃的电容器是电子垃圾的重要组成部分。这些旧电容器不仅含有有害化学物质，而且还富含经济上可回收的贵金属，如铌和银。本研究旨在加强对废弃电容器的分类，以便更有效地回收和资源回收。传统的电容器分类方法主要依靠人工识别，效率低，准确度有限。为了提高分类的效率和准确性，本研究首次引入激光诱导击穿光谱（LIBS）技术与机器学习相结合的电容器分类方法。反向传播人工神经网络（BP-ANN）算法可以用于自动识别和分类废弃电容器。为了获得更好的性能，我们开发了一种新的算法，称为优化特征提取方差算法（OFEVA），该算法通过显着提高分类模型的准确性来解决现有方法的局限性。与传统主成分分析（PCA）的主成分得分数据进行训练相比，OFEVA训练具有更高的分类精度和计算效率。这种创新的方法不仅有助于提高废弃电容器的回收率，减少环境污染，而且为资源的再利用提供了重要的技术支持，从而为环境保护和资源循环利用领域做出了重要贡献。此外，本文还首次对纯铌的谱线进行了标定，为进一步的光谱研究提供了重要的数据。

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引用次数: 0

Dry solidification of chloride salts and heavy metals in waste incineration fly ash by mayenite 垃圾焚烧飞灰中氯盐和重金属的梅氏岩干固化研究。

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.12.035

Xin Li , Dongdong Ge , Nanwen Zhu , Yukai Zong , Shi Cheng , Pin Zhou , Min Jiang , Yun Gu , Shouqiang Huang

There are hazardous substances such as chloride salts and heavy metals in the municipal solid waste incineration fly ash (WIFA). During thermal treatment, the concentrated chlorides promote the volatilization of heavy metals, increasing the ecological risk. The water washing method is also employed as a pre-treatment for WIFA, but a substantial volume of wastewater with high chloride content is produced that poses challenges for effective treatment. This study integrates chemical stabilization with heat treatment method and suggests the utilization of a calcium aluminum oxide-mayenite (CA) for the solidification of chloride salts and heavy metals in WIFA. The experimental results indicate that adding CA for heat treatment has a significant solidification effect on chlorides. Under the conditions of WIFA: CA mass ratio of 1: 1 and temperature of 1200 °C, the chloride ions were solidified by forming Ca₁₂Al₁₄O₃₂Cl₂, with a fixation efficiency of up to 85 %, and most of the chlorides in WIFA became insoluble instead of soluble. Most of the heavy metals in WIFA were immobilized and doped into the crystal structure of CA, forming the catalytic metal-rich Ca₁₂Al₁₄O₃₂Cl₂ phase, which was subsequently applied to the degradation of chlorobenzene. Under an initial concentration of 512 ppm, the degradation efficiency of chlorobenzene reached 50.4 %. Through the introduction of CA, not only the solidification of chloride and heavy metals is achieved, but the high-value resource utilization of the final heat treatment product is also realized, providing a new method for the disposal of fly ash.

城市生活垃圾焚烧飞灰中含有氯化物盐和重金属等有害物质。在热处理过程中，高浓度氯化物促进重金属的挥发，增加了生态风险。水洗法也被用作WIFA的预处理方法，但会产生大量氯化物含量高的废水，这给有效处理带来了挑战。本研究将化学稳定与热处理方法相结合，提出了一种钙铝氧化物-梅氏岩（CA）用于氟化铝中氯化物盐和重金属的固化。实验结果表明，在热处理过程中加入CA对氯化物有明显的凝固效果。在WIFA: CA质量比为1:1、温度为1200℃的条件下，氯离子通过形成Ca12Al14O32Cl2进行固化，固化效率高达85%，WIFA中的氯离子大部分由可溶变为不溶。WIFA中的大部分重金属被固定并掺杂到CA的晶体结构中，形成富金属的催化物Ca12Al14O32Cl2相，随后应用于氯苯的降解。在初始浓度为512 ppm时，对氯苯的降解效率达到50.4%。通过CA的引入，不仅实现了氯化物和重金属的固化，还实现了最终热处理产物的高价值资源化利用，为粉煤灰的处理提供了一种新的方法。

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引用次数: 0

Efficient reduction of electric arc furnace dust by CO/H2 derived from waste biomass: Biomass gasification, zinc removal kinetics and mechanism 废生物质CO/H2高效还原电弧炉粉尘：生物质气化、除锌动力学及机理

IF 7.1 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Waste management

Pub Date : 2025-02-01 DOI: 10.1016/j.wasman.2024.11.049

Xingnan Huo , Dingzheng Wang , Jinlin Yang , Shaojian Ma

Electric arc furnace dust (EAFD) represents hazardous solid waste that poses substantial environmental risks, necessitating the urgent development of green and efficient recycling methods. Biomass, a renewable and carbon–neutral resource, offers a viable solution. This study proposes a synergistic process that integrates biomass gasification with reducing EAFD. The kinetics of zinc removal during the process were examined, and the synergistic reaction mechanisms between biomass gasification and EAFD reduction were analyzed through PY-GC/MS, SEM/EDS, XRD, TEM, and thermodynamic calculations. The findings demonstrated an exceptional zinc removal efficiency of 99.88%, governed primarily by interfacial chemical reactions. The synergistic reactions mutually enhanced the reduction of EAFD and the reforming of pyrolysis products. Furthermore, the process achieved low carbon emissions owing to the carbon cycle established through coupling reactions between the dust and biomass.

电弧炉粉尘是危害环境的有害固体废物，迫切需要开发绿色高效的回收方法。生物质，一种可再生的碳中性资源，提供了一个可行的解决方案。本研究提出了一种将生物质气化与减少EAFD相结合的协同过程。通过PY-GC/MS、SEM/EDS、XRD、TEM和热力学计算分析了生物质气化与EAFD还原之间的协同反应机理。研究结果表明，锌的去除效率为99.88%，主要由界面化学反应决定。协同反应相互促进了EAFD的还原和热解产物的重整。此外，由于通过粉尘和生物质之间的耦合反应建立了碳循环，该过程实现了低碳排放。

引用次数: 0