Waste management最新文献

Preparation of activated carbons is an important way to utilize municipal sludge (MS) resources, while drying is a pretreatment method for making activated carbons from MS. In this study, machine learning techniques were used to develop moisture ratio (MR) and composting temperature (CT) prediction models for the thermally assisted biodrying process of MS. First, six machine learning (ML) models were used to construct the MR and CT prediction models, respectively. Then the hyperparameters of the ML models were optimized using the Bayesian optimization algorithm, and the prediction performances of these models after optimization were compared. Finally, the effect of each input feature on the model was also evaluated using SHapley Additive exPlanations (SHAP) analysis and Partial Dependence Plots (PDPs) analysis. The results showed that Gaussian process regression (GPR) was the best model for predicting MR and CT, with R² of 0.9967 and 0.9958, respectively, and root mean square errors (RMSE) of 0.0059 and 0.354 ℃. In addition, graphical user interface software was developed to facilitate the use of the GPR model for predicting MR and CT by researchers and engineers. This study contributes to the rapid prediction, improvement, and optimization of MR and CT during thermally assisted biodrying of MS, and also provides valuable guidance for the dynamic regulation of the drying process.

Smouldering is a novel, low-energy, low-cost technology for disposing of sludge with high moisture. Currently, related researches focus on the lab-scale with batch disposal, but more reports about the pilot-scale with continuous process need to be done. Based on our previous research, this study further enlarged the pilot-scale smouldering reactor height from 1.2 m to 2.0 m and provides insight into the smouldering performance under long-term continuous operation and the influence of the discharging residue interval and Darcy velocity. The temperate evolution shows that the discharging residue interval significantly affects the reaction location stability due to the difference between the smouldering upward velocity and the feed-stock descent velocity. Furthermore, the unburnout content, heavy metal in the smouldering-derived residue, the non-condensable flue gas concentrations (O₂, CO, NO_x, VOCs, dioxin), and the components of the condensable liquid are deeply investigated. The pilot-scale measurements show that 30 s and 40 s for the discharging residue interval under the studied operating condition may be reasonable for the high (3.5 cm/s and 5.0 cm/s) and low (2.5 cm/s) Darcy velocity, respectively. Comprehensively, the condition with 30 s for the discharging residue interval and 3.5 cm/s for the Darcy velocity is suggested in the future actual application.

Food packaging waste significantly impacts global environmental changes, prompting the adoption of a green circular economy approach. Recycling packaging waste is a critical element of this strategy. However, it faces challenges related to the quality of recycled materials and concerns about their safety. Thus, this review aimed to highlight different analytical methods alone or in combination to evaluate the quality of the recycled material. Furthermore, the safety and health aspects related to the migration of contaminants and their relevant regulations have also been discussed. An important parameter while selecting an appropriate recycling method is the composition and nature of the recyclate, for instance, HDPE (High-Density Polyethylene), PET (Polyethylene Terephthalate), and PP (Polypropylene) materials can be recycled using mechanical and chemical recycling, however, PVC (Polyvinyl Chloride) and PS (Polystyrene) present challenges during mechanical recycling due to lower molecular weight and complex compositions, thus are often downcycled into lower-grade products. Still, recycled papers can be more problematic than recycled plastics due to the nature of the materials and the impact of recycling. The literature review suggested that three quality properties i.e., presence of low molecular weight compounds, degree of degradation, and composition should be analyzed by using different spectroscopic, thermo-mechanical, and chromatographic techniques to obtain a detailed understanding of recycled material quality. Furthermore, recycling should be done in such a way that the migration of contaminants should be lower than the migratory limits set by the relevant authorities to avoid any toxicological effects.

The effective recovery of valuables from anodes coming from spent lithium-ion batteries (LIBs) is of great importance to ensure resource supply and reduce the environmental burden for recycling. In this work, a simple and low energy consumption roasting method was proposed by employing low-temperature eutectic NaOH-KOH as reaction medium, in order to simultaneously separate graphite from Cu foils, extract lithium from it and set it up for reuse as environmental catalyst through one-step water washing process. Our results show that polyvinylidene difluoride (PVDF) was effectively deactivated due to dehydrofluorination/carbonization at a relatively low temperature and short time (150 °C, 20 min) when a mass ratio of 1:1 for eutectic NaOH-KOH to spent LIBs anodes was used, yielding 97.3 % of graphite detached. Moreover, a remarkable lithium extraction efficiency of 93.2 % was simultaneously obtained. Afterwards, the reusability of the recycled graphite was tested by employing it as a catalyst for the treatment of a contaminant organic dye (Rhodamine B) in the presence of NaClO. Our results show that a superior NaClO activation was obtained with the addition of recycled graphite, being this fact closely associated to the abundant active sites formed during the long-term charging/discharging cycles in the original battery. The alkaline-mediated roasting process presented in this work presents an energy-saving scheme to efficiently recover useful components from spent anodes, whereas the reusability example highlighted a useful option for repurposing the severely damaged graphite as an environmental catalyst rather than disposing it in landfills, turning waste into a valuable material.

Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with.

Brewers’ spent grain (BSG) is the main byproduct from the brewing industry, which accounts for 85 % of the total waste generated during beer production. This lignocellulosic material is traditionally used as livestock feed and sold at a low price. However, BSG can be used as a low-cost feedstock for the production of bioactive molecules and chemicals precursors, upgrading the value of this byproduct. In this context, BSG is a promising feedstock for the extraction of antioxidants like ferulic acid (FA) and p-coumaric acid (p-Cu). The effectiveness of three hydrolysis treatments were evaluated for the extraction of FA and p-Cu from BSG, namely enzymatic (based on the synergistic cooperation between a feruloyl esterase and an endo-1,4-β-xylanase), alkaline and hydrothermal. The hydrothermal treatment produced the highest extraction yields (7.2 g/kgBSG and 1.4 g/kgBSG for FA and p-Cu, respectively) in a short extraction time (an hour). On the other hand, enzymatic hydrolysis extracted 4.3 g/kgBSG for FA and negligible yields for p-Cu in 4 h of incubation at 25 °C. Yields of 5.5 g/kgBSG for FA and 0.6 g/kgBSG for p-Cu were obtained in more than 5 h of alkaline treatment at 120 °C. The mass and energy balances revealed the high dependence of the operating costs on the concentration of BSG used during the extraction process, with costs of 34.5 €, 6607 € and 205.5 € per kg of FA for the chemical, enzymatic and hydrothermal extraction methods at 100 kg BSG/m³.

Lignocellulosic biomass has a promising role in a circular bioeconomy and may be used to produce valuable molecules for green chemistry. Lignocellulosic biomass, such as food waste, agricultural waste, wood, paper or cardboard, corresponded to 15.7% of all waste produced in Europe in 2020, and has a high potential as a secondary raw material for industrial processes. This review first presents industrial lignocellulosic waste sources, in terms of their composition, quantities and types of lignocellulosic residues. Secondly, the possible high added-value chemicals obtained from transformation of lignocellulosic waste are detailed, as well as their potential for applications in the food industry, biomedical, energy or chemistry sectors, including as sources of polyphenols, enzymes, bioplastic precursors or biofuels. In a third part, various available transformation treatments, such as physical treatments with ultrasound or heat, chemical treatments with acids or bases, and biological treatments with enzymes or microorganisms, are presented. The last part discusses the perspectives of the use of lignocellulosic waste and the fact that decreasing the cost of transformation is one of the major issues for improving the use of lignocellulosic biomass in a circular economy and green chemistry approach, since it is currently often more expensive than petroleum-based counterparts.

Plastic waste poses a critical environmental challenge for the world. The proliferation of waste plastic coffee pods exacerbates this issue. Traditional disposal methods such as incineration and landfills are environmentally unfriendly, necessitating the exploration of alternative management strategies. One promising avenue is the pyrolysis in-line reforming process, which converts plastic waste into hydrogen. However, traditional pyrolysis methods are costly due to inefficiencies and heat losses. To address this, for the first time, our study investigates the use of microwave to enhance the pyrolysis process. We explored microwave pyrolysis for polypropylene (PP), high-density polypropylene (HDPE), and waste coffee pods, with the latter primarily comprising polypropylene. Additionally, catalytic ex-situ pyrolysis of coffee pod pyrolysis over a nickel-based catalyst was investigated to convert the evolved gas into hydrogen. The single-stage microwave pyrolysis results revealed the highest gas yield at 500 °C for HDPE, and 41 % and 58 % (by mass) for waste coffee pods and polypropylene at 700 °C, respectively. Polypropylene exhibited the highest gaseous yield, suggesting its readiness for pyrolytic degradation. Waste coffee pods uniquely produced carbon dioxide and carbon monoxide gases because of the oxygen present in their structure. Catalytic reforming of evolved gas from waste coffee pods using a 5 % nickel loaded activated carbon catalyst, yielded 76 % (by volume) hydrogen at 900 °C. These observed results were supported by elemental balance analysis. These findings highlight that two-stage microwave and catalysis assisted pyrolysis could be a promising method for the efficient management of waste coffee pods, particularly for producing clean energy.

Whether it be to measure their value before a trade, to calculate yields and optimize the recycling process or to check for the presence of harmful substances, Waste Electronic and Electric Equipments (WEEE) need to be characterized. Sampling can give an accurate assessment of the grade of a batch of WEEE, but quantifying the uncertainty around this estimate can be delicate. Pierre Gy’s sampling theory of particulate matter studies how the latter is affected by the physical and chemical properties of the studied objects. However, its application requires a deep understanding of the correlations existing between their size, shape, volume, density, mass and grade, which are still unclear for WEEE fragments. Although average information is typically available on batches of WEEE, a more detailed description would be necessary to gain insight into such relationships.

To start filling the gap, this paper focuses on the fine characterization of two different batches of waste printed circuit boards, crushed into pieces of about 10 mm. One by one, over 5,000 fragments were sampled, photographed and analyzed. Their individual mass, density, volume, thickness, surface, width and length were all measured separately. Based on their appearance, they were also sorted into four heuristic categories: plastic, metal, circuit boards and electronic components. Descriptive statistics of this novel granulometric database are shown here, throwing light on the unique correlations between the studied parameters and exhibiting a peculiar mass-size law. They point to new avenues on how to adapt Gy’s sampling model to WEEE.

The significant production of municipal solid waste incineration fly ash (MSWI FA) underscores the importance of developing efficient solidification materials. This study employed MgO and CaO for immobilizing MSWI FA (with a 70% fly ash incorporation), and the immobilization effect was compared with that of Portland cement (PC). Experimental findings revealed that MgO exhibited the most effective stabilization for heavy metals (Cd, Cu, Pb, and Zn) compared to CaO and PC. XRD, FTIR, TG, and SEM analysis indicated that the principal hydration products in MSWI FA binders solidified with MgO, CaO, and PC were Mg(OH)₂, CaCO₃, and C-S-H gel, respectively. Mg(OH)₂ efficiently immobilized heavy metals through chemical complexation and surface adsorption mechanisms. The MgO-treated MSWI FA demonstrated the highest residual fractions and the lowest easily leachable fractions. Moreover, the leaching characteristics of heavy metals were significantly influenced by the pH level, so MgO-treated MSWI FA with a leachate pH of 9.18 achieved the precipitation and stabilization of most heavy metals. In summary, this study provided an effective material selection for MSWI FA immobilization and presented a novel strategy for MSWI FA management.