Journal of Material Cycles and Waste Management最新文献

Widespread concerns about plastic pollution have compelled many countries to implement measures aimed at reducing plastic waste (PW) leakage into the environment. Setting 2023 as the base year, the current study employs material flow analysis (MFA) to investigate how the PW situation in Zimbabwe would respond to different management options. The study involved constructing PW flows in an MFA model and re-constructing them under five different scenarios to evaluate how the PW situation changes. Five different indicators are used in comparing outcomes across the scenarios. The results show that increasing PW collections from the current 3.7–10% will increase the availability of plastic recyclates to formal recyclers by 140%, and will raise the country's overall PW recycling rate to 19.8%. However, implementing this measure alone cannot sufficiently reduce plastic pollution. Combining waste-to-energy (WtE) and increasing PW recycling is a necessary interim measure for drastically reducing PW stocks by 40% and creating enough feedstock for a WtE plant with an energetic potential of 3.4 × 10⁶ GJ per annum. The study focuses on Zimbabwe, but the lessons learned can be applied to various contexts and assist similar developing countries in decisively reducing plastic pollution.

This study explores the potential of acrylonitrile butadiene styrene (ABS) resin, a bromine-containing plastic, for selectively separating PbO from a ZnO-PbO mixture. Thermodynamic calculations suggested the susceptibility of both PbO and ZnO to bromination by HBr from ABS resin. Initial trials showed limited PbO and ZnO volatilization. Combusting ABS resin and dust mixtures converts approximately 40 wt% of PbO to PbBr₂, but PbO volatilization selectivity remains below 30% due to concurrent ZnO volatilization. This low selectivity is due to the inhibiting effect of char and CO gas on PbBr₂ volatilization and the enhancement of ZnO volatilization. To improve PbO volatilization selectivity, operational parameters were varied. Increasing ABS resin size, decreasing pellet size, and altering the heating method raised PbO volatilization selectivity by over three times, with more than 60 wt% PbO volatilized. Microscopic analysis confirmed PbO bromination and volatilization as lead bromide compound (e.g., PbBr₂), while ZnO underwent direct volatilization through a carbothermic reaction. This study shows that optimizing operational parameters can selectively separate heavy metals using bromine-containing plastics. For practical application in steelmaking dust, it is crucial to examine the effects of coexisting Fe compounds.

Advanced artificial intelligence (AI) technologies have bestowed numerous advantages upon our daily lives. Despite the ongoing efforts of various institutions urging responsible waste distribution for the preservation of our planet, completely resolving the waste problem remains a formidable challenge. This paper endeavors to present a solution through the integration of AI into waste distribution systems. We introduce WimpleBin, an AI-based recycle bin, designed to accurately classify waste streams following training with machine learning algorithms. Utilizing the YOLOv5 framework, we train WimpleBin with the collected data to accomplish our objectives. The 81% accuracy achieved in real-world scenarios demonstrates WimpleBin’s impressive ability to effectively categorize different types of waste.

Most studies on the pyrometallurgical recovery of Zn from spent batteries have focused on assessing the recovery of Zn. However, the separation behavior of Zn from the produced black powder remains unknown. In this study, a pyrometallurgical process for recycling Zn and MnO from the black powder obtained by shredding spent alkaline and Zn–C batteries was developed. The black powder was washed with deionized water to remove KCl and subsequently used for recovering Zn and MnO. Preliminary thermodynamic calculations were performed using Factsage 8.2 and HSC 6.0 to set temperature conditions for the experiment. The washed black powder was placed in an electric vertical resistance furnace and heated to 800 ~ 1300 °C. The ZnO present in the black powder was reduced to Zn_(g) through carbothermal reduction, and then, Zn_(g) was rapidly condensed in a capture cup to recover Zn powder. After heat treatment at 1300 °C for 1 h, 99.39% of the Zn powder was recovered with 98.16% purity. Furthermore, MnO of 99.05% Mn purity remained as a residue from the black powder in the crucible. The results of this study present a crucial step in improving the resource recovery process while minimizing environmental impact, contributing to the advancement of environmentally friendly recycling technologies.

The objective of this study is to develop a highly efficient technology for producing hydrogen from sewage sludge using chemical-looping technology. In this study, laboratory-scale gasification experiments were conducted on sewage sludge samples. The effects of CaO as a carrier of oxygen or carbon (C) and as a gasification accelerator were evaluated. Lab-scale tests using a tube-type electric furnace showed that gasification of CaO-mixed dry sludge with steam resulted in substantial absorption of CO₂ by CaO at 600 ℃ and 700 ℃ and the concentration of H₂ in the gas reached 70 vol.%. The gasification was significantly accelerated in CaO-mixed steam gasification compared with pyrolysis gasification and steam gasification of dry sludge in the absence of the CaO mixture. The hydrogen gas (H₂) recovery rate also increased, and tar with a high C weight ratio and high calorific value was recovered. Almost the entire C in the sewage sludge was gasified at 800 ℃. Therefore, this method (pyrolysis with steam and CaO) was shown to be effective as one of the hydrogen production methods from sewage sludge.

The present research has considered natural zeolite as the base member of the brick composition, and sugarcane bagasse ash, as an additive. Ceramic brick samples consisting 0, 10, and 20% sugarcane bagasse ash by mass of the samples, and natural zeolite as the rest of the total mass were cast. The samples were subjected to uniaxial dry pressing, sintered, and prepared in an electric furnace at temperatures of 950–1050 °C. Mineralogical composition, chemical composition, and particle size distribution of natural zeolite and sugarcane bagasse ash were investigated by X-ray diffraction (XRD), X-ray fluorescence (XRF), and granulation test, respectively. Also, the effect of different compositions and different sintering temperatures on the samples’ physical and thermal properties were studied. The results showed that the thermal conductivity of the samples ranges from 0.89 to 1.46 W/mk. Samples that were sintered at 950 °C and had a higher sugarcane bagasse ash percentage showed a lower thermal conductivity. Also, the samples exhibited compressive strength, ranging from 2 to 30.4 MPa. The ceramic brick, composed of 20% sugarcane bagasse ash, subjected to a sintering temperature of 950 °C, exhibited optimal characteristics. This particular sample obtained a thermal conductivity of 0.89 W/mk and a density of 1.3 g/cm³.

As one of the main components of urban waste, the appropriate disposal of construction waste is crucial for the sustainable development of cities. The recognition and classification of construction waste are crucial components of construction waste management and lay the foundation for high-value applications. This paper utilized unmanned aerial vehicle (UAV) aerial images and the YOLO model to recognize construction waste on-site. A dataset of indoor scattered, indoor dense, and demolition site conditions was established. The impact patterns of epoch number, initial learning rate, and batch-size on the model were discussed, and the optimal parameter values were determined. The influence of training dataset composition was analyzed, indicating that appropriately adding indoor dense condition images helped improve the convergence speed and recognition accuracy. In the model performance validation, the comparison with manually sorting results revealed a high accuracy in recognizing the five types of construction waste. The recall of all five types was around 0.8. The validation results showed that the model can efficiently and accurately recognize and classify construction waste in demolition site images. The proposed method can aid in the rapid assessment and dynamic monitoring of construction waste, thereby enhancing the efficiency of waste management and recycling.

The development of civil infrastructure in 2016–2021 increased cement and concrete consumption across Indonesia. The reduction of global warming effects can be expedited by providing environmentally friendly cement and concrete alternatives complemented with embodied energy and carbon assessment references. In this study, we investigated the local embodied energy and carbon emission with a boundary of cradle-to-gate for cements and that of cradle-to-site for ready-mix concretes in Indonesia. The production process data were collected from cement manufacturers and ready-mix concrete companies. The analysis starts with the ordinary Portland cement (OPC) and non-OPC cement, and continues to the lightweight OPC concrete and the normal-weight blended concrete. The embodied energy and carbon were quantified. The results showed that those of OPC were the highest, followed by the non-OPC cement of Portland pozzolan cement (PPC), the Portland cement composite (PCC), and the masonry cement. The available low-energy and carbon concrete mixes were found to be a combination of a high-range water reducer admixture, a supplementary cementitious material (SCM), and cement. The lightweight OPC concrete had similar embodied energy with the reference OPC concrete, but its embodied carbon was up to 11.1% lower than the reference OPC. The normal-weight blended PCC concrete with optimum SCM levels reduced embodied energy and carbon by 53.8% and 58.2%, respectively, compared to the reference OPC concrete. The reference PPC concrete is considered an alternative for assessing the embodied energy and carbon of blended concrete in the building design stage.

Most optimization models developed to solve the long-term planning of municipal solid waste management system (LPMSWMS) problems have some disadvantages, such as the inability to model the problem realistically and the failure of the final decisions to provide global optima due to various mathematical limitations. This study presents an optimization approach for realistically modeling and solving the LPMSWMS problems. This new approach takes into account a non-linear mathematical formulation for the LPMSWMS and solves the model into two main stages. In the first stage, the linear relaxation of the model is used, while the non-linear model formulation is used in the second stage to obtain a more realistic solution for the LPMSWMS. According to the findings, the new optimization approach obtained results for small model volumes in longer times than the reference non-linear model structure. On the other hand, the new approach makes it possible to obtain global optima by producing exact solutions at large model volumes, while the reference non-linear model structure cannot produce any solution. This advantage of the new approach for large model volumes can significantly benefit decision-makers in solving big-scale problems that are likely to be encountered in the real world by giving global optima.

Household food waste (HFW) poses a significant global challenge for industries and governments due to its adverse environmental, societal, and individual impacts. This study makes a novel contribution to the existing literature by integrating the value-attitude-behavior (VAB) approach and values operationalized as food-related motives within an extended goal-framing framework to explicate consumers' attitudes toward food waste reduction (AFW) and HFW. Specifically, the investigation delves into pro-environmental (environmental protection and animal welfare) and pro-social motives (political, religious, and moral motives), elucidating normative goals, alongside pro-self motives, which encompass gain (health) and hedonic goals. Data were collected from 600 consumers residing in six major cities across Vietnam through a self-administered questionnaire. Structural equation modeling was utilized to assess the proposed hypotheses. The findings underscore the significant contribution of AFW to reducing HFW and highlight its substantial influence from these motives with varying valence. This study enhances our understanding of how values manifest in food-related motives and extend goal-framing theory, emphasizing the importance of targeting these motives in interventions aimed at mitigating HFW while enhancing AFW.