Waste management最新文献

Black soldier fly larvae (BSFL) efficiently convert biowaste into valuable animal feed. Sustainable and reliable bioconversion is desirable to achieve optimal economic and environmental outcomes. Thus, science and industry require an accessible research platform to study complex bioconversion processes under conditions mirroring industrial-scale settings. In this study, industry-relevant respiration chambers were designed, tested, and replicated for BSF feeding trials. Each open-circuit chamber housed three industrial rearing crates. The substrate/frass and air temperature, mass change, NH₃ and CO₂ emissions, and relative humidity were measured. The design was validated for CO₂ recovery, airtightness, airflow homogeneity, and BSFL performance using firstly, a uniform control substrate and secondly, uniform food waste across four parallel chambers. In a third trial, the composition of food waste was varied across parallel chambers to detect differences in metabolic processes. For trials using uniform substrates, low variability across chambers in performance parameters confirmed the reproducibility and comparability of the design (e.g. bioconversion rate: <1%, final larval mass: ≤2 mg, standard deviations, dry matter based). In contrast, the trial with varying food waste compositions showed a strong effect on average substrate/frass temperature (e.g. 31.5 °C vs 41.8 °C) and final dry larval mass (e.g. 67 mg vs 40 mg). This is the first study to systematically assess heat generation directly from heterogeneous food waste, a crucial parameter for efficient BSFL bioconversion. These chambers provide an opportunity for science and industry to thoroughly assess and understand the metabolic bioconversion characteristics. The findings are key for the optimisation of sustainable bioconversion processes.

In this study, ex-situ catalytic pyrolysis of oxygen-containing polycarbonate (PC) was conducted to prepare carbon nanotubes (CNTs) and H₂-rich syngas. This study examined the influence of the active metal components (Ni and Fe), catalyst pre-reduction, and pre-deoxygenation of pyrolysis volatiles on the catalytic performance and mechanism. Results show that the reductive constituents in pyrolysis volatiles make it difficult to reduce the Fe oxides, thus hindering the CNTs growth on Fe catalysts, compared to Ni catalysts. H₂ pre-reduction of Ni and Fe catalysts enhances the generation of CNTs and syngas. The pre-reduced Fe catalyst exhibits better carbon deposit performance, reaching 263 mg/g_plastics. The pre-reduced Ni catalyst better facilitates the reforming reaction of CO₂ and H₂O, resulting in higher syngas yields of 32.75 mmol/g_plastics, with a volume proportion of 94.4 vol%. The addition of the deoxygenation catalyst Ni/HZSM-5 promotes the growth of CNTs with fewer defects and higher graphitization on Ni catalysts. The excess CO₂ and H₂O generated by the introduction of Ni/HZSM-5 may oxidize the Fe⁰ on pre-reduced Fe catalysts, inhibiting the growth of CNTs. The mechanism of the growth of CNTs and syngas from PC is also explored. The findings can provide theoretical guidance for the disposal of waste plastics.

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.

The identification of recyclable resources are extremely important to balance the growing demand for polymer composite 3D printing and sustainable manufacturing. In the present study, SS 316L powder particle infused PLA filaments are fabricated by deriving PLA from discarded bi-material extrudates, adopting solvent mixing methodology. The matrix reclaimability, composite feedstock fabrication, extrudability and printability are investigated by increasing the solid loading from 10 - 40 wt%. Outcomes of the FTIR for 'PLA gel' and 'extrudate dissolved gel' are identical and confirms the matrix reclaimability. Essential characterization studies like XRD, TGA, and DSC are carried out for composite feedstock. The reinforcement dispersion in composites is quantified with the help of microscopy results. The melt rheological studies reveal that all the extruded filaments exhibit shear thinning over a shear rate of 50 s^-1 and are compatible with 3D printing. The tensile strength is improved by 22.4% after adding 10 wt% reinforcement to the recycled PLA. The economical benefits are evaluated by comparing the composite filament fabrication with the pure PLA matrix. The study concludes recommending viscous modifiers to improve filament processability and increase loading capacity beyond 40 wt% for indirect metal additive manufacturing.

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. Ourwork 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 imagesused 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.

Managing the diverse waste fractions generated by households presents a significant environmental and logistical challenge. One widely adopted solution is waste sorting at the source, where residents are required to separate their waste into designated containers. The success of this strategy depends on the extent of adoption and the behaviour of residents. Waste separation is a complex activity influenced by various interrelated factors. While the Theory of Planned Behaviour (TPB) has been effectively applied to characterise waste-sorting behaviour, it primarily focuses on internal psychological mechanisms, often overlooking environmental factors such as the placement of waste bins or the condition of sorting stations-critical elements for spatial planning. To bridge this gap, this study presents an agent-based model (ABM) that simulates residential waste sorting in urban scenarios, incorporating TPB for the agents' behavioural architecture (residents). Three features distinguish this ABM from previous efforts: (i) Agents in the model are residents and not aggregated households, allowing for a one-to-one integration with TPB; (ii) the ABM bridges the gap between individual waste sorting behaviour extracted by TPB and outcomes quantifiable through waste sorting metrics; and (iii) the ABM is spatially explicit, enabling the exploration of various urban scenarios. The ABM was applied to two urban areas with differing population densities, demonstrating that changes in bin placement impacts sorting behaviour, and proximity to recyclable waste bins influences the correct sorting of residual waste. This study illustrates how modelling the interaction between the urban environment and waste sorting behaviour can reveal the impact of individual residents' actions on overall waste sorting performance.

The liquid fraction of digestate (LFD) from anaerobic digestion of food waste contains high nitrogen concentrations, and in some countries, the LFD is treated as wastewater. We modelled alternative LFD treatments, including pretreatment with the partial nitritation Anammox (PNA) process. The PNA effluent is discharged to the sewers to undergo further treatment by conventional nitrification and (post- or pre-) denitrification. Life-cycle inventories were developed for the LFD treatment alternatives, including N₂O emissions and electricity consumption estimates. The climate change (CC) impact was estimated using life cycle assessment in three different energy systems ranging from fossil-based to fully renewable. In the fossil energy system, pretreatment with PNA was attractive, while in the more renewable energy systems, the PNA process did not improve the CC account due to high N₂O emissions. Pre-denitrification is the most attractive LFD treatment technology in a fully renewable energy system. Linking the LFD treatment to the anaerobic digestion of food waste showed that LFD treatment is a significant contributor to the overall CC account. As we move towards less fossil-based electricity, the anaerobic digestion of food waste constitutes a CC load of 350-450 kg CO₂-eq/tonne biowaste, of which up to a third can be attributed to the LFD treatment. The N₂O emissions are the main contributor, constituting up to 50 % in a fossil-based energy system and even higher in a renewable energy system. We conclude that the LFD treatment must be addressed in assessing anaerobic digestion when the LFD is discharged to the sewer. Our study also points to the need to find alternative ways of managing the LFD.

The primary goals of this paper are to facilitate data-driven decision making in solid waste management (SWM) and to support the transition towards a circular economy, by providing estimates of the composition and quantity of waste. To that end, it introduces a novel two-phase strategy for predicting municipal solid waste (MSW). The first phase predicts the waste composition, the second phase predicts the total quantity, and the two predictions are combined to give a comprehensive waste estimate. This novel approach overcomes limitations of existing methods that rely on material-specific quantity data, facilitating the prediction of dozens of waste material streams; existing methods typically classify MSW into no more than 10 categories, and often reduce it to a single aggregate total. To implement this strategy, the proposed study utilizes publicly available data encompassing demographic, economic, and spatial predictors, in conjunction with waste sampling reports. In addition, it develops a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to estimate the MSW composition across 43 comprehensive material categories. The LASSO model is designed to predict MSW composition distinctly from quantity. The model's capability is demonstrated through case studies, showcasing its potential to provide detailed waste estimates at the U.S. county level.

An analysis was conducted using nationwide survey data to evaluate how material recovery facilities (MRFs) operations vary regionally and with scale. The survey characterized materials, processes, and energy use involved with operations, and revenue for recyclables. This is the first nationwide analysis of MRFs in the US that accounts for mass processed, energy consumed, and revenue. Of a population of 521 MRFs, 48 responses representing MRFs from five US regions were received and analyzed (9.2 % response rate). Responses were analyzed by size according to yearly mass of inbound materials (small: <1,000 Mg/year, medium: 1,000-10,000 Mg/year, and large: >10,000 Mg/year). Most MRFs identify as single-stream; source from residences; utilize tipping floors, picking lines, baling and warehousing; and are powered by electricity. Most revenue and inbound mass (>50 %) came from fiber (cardboard and paper). Glass had little revenue, and plastics were difficult to transition to market. Percent residue ranged from 1-39 %, averaged < 20 %, and increased as the mass of inbound material increased. Large MRFs reported more sources of material, employed advanced sorting technology, had greater plastics revenue (33 % versus 5 % for small MRFs), and had more market access for plastics compared to small MRFs. Large MRFs had two orders of magnitude less annual electricity consumption per Mg recyclables than small MRFs (5-90 kWh/Mg versus ∼ 300-550 kWh/Mg). Results demonstrate environmental and economic benefits of larger-scale MRFs, which could be implemented more broadly in the US through regional hub-and-spoke arrangements for collecting and processing recyclables, lowering energy consumption and increasing revenue for recyclables.

Lignocellulosic biorefinery technology requires minimum energy consumption and wastewater generation to overcome challenges in industrial applications. This study established a rigorous model and a comprehensive physical property database of dry biorefining process on Aspen Plus platform for production including L-lactic acid, citric acid, sodium sugar acids, amino acid, and ethanol based on the experimental data. Full evaporation of wastewater (FEW) approach was proposed to completely replaced the external steam supply, and significantly reduced the freshwater input by 67% ∼ 85% and wastewater generation by 64% ∼ 89%, depending on the specific products. The carbon-neutral heat energy from lignin residue combustion generates an extra heat output of 1.098 ∼ 4.772 GJ per ton of dry wheat straw (DW) after all the heat energy needs of the biorefinery process and FEW treatments are satisfied, equivalent to a reduction of 0.219 ∼ 0.952 kg CO₂ eq/kg DM emission. This study provided a self-consistent solution for water and energy balance in biorefinery processes.