Resources, conservation & recycling advances最新文献

Small island developing states (SIDS) face persistent challenges in managing household solid waste due to inadequate waste management infrastructure. This study investigates the existing solid waste management practices in Nasinu Town Council, Fiji, through waste characterization survey (WACS) and life cycle assessment (LCA), accompanied by geographic information system (GIS) analysis to evaluate potential waste treatment facilities. Various strategies are explored, including recycling inorganic waste and converting organic waste into energy. Landfill gas recovery is identified as a significant contributor to reducing toxic gases like carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄) emissions. Furthermore, treating organic waste reduces landfill volume and minimizes the release of pollutants. The study emphasizes the importance of supportive policies for effective solid waste management and highlights how organic waste treatment can improve waste management in Nasinu Town. This research, unique among SIDS studies, provides valuable insights and replicable technologies applicable to the study area and the broader Pacific Region, with the potential to significantly improve solid waste management practices.

Along food supply chains, one-third of global food production is wasted annually: circular economy can be applied to prevent and recover food waste. The literature has explored food waste from many perspectives; however, no attention has been devoted to understanding how the intrinsic characteristics of food products influence food waste generation and valorization. This study proposes a classification of food products based on circular economy principles derived from a systematic literature review. The classification sheds light on how the intrinsic variability of food products influences food waste generation and recovery along the supply chain. The characteristics that drive differences in terms of food waste are identified by defining two product groups for each step of the chain (primary production: plant origin and animal origin; manufacturing: minimally processed and processed; distribution: ambient temperature and controlled temperature; retail: short shelf life and long shelf life). This stresses the intertwining of food waste with supply chain operations. Moreover, within the same supply chain stage, food waste causes and circular economy actions vary greatly depending on the product characteristics. The review also reveals how the most relevant causes within each product category correspond to a high relevance of practices addressing these causes. The adopted perspective represents a novel contribution to knowledge, providing a clear discussion of the variability of food waste along the supply chain and unveiling aspects requiring further research. From a practical standpoint, the classification can empower food industry actors to develop circular economy actions through an appropriate understanding of product characteristics.

Ethylene propylene diene rubbers (EPDM) have gained substantial attention in automotive and industrial applications owing to their exceptional resistance against weathering and heat. Despite their advantages, the elastomeric nature of EPDM poses challenges in its recycling due to the presence of crosslinks in their chemical structure, preventing them from melting. To overcome this issue, devulcanized EPDM (EPDMd) has been developed, characterized by the effective breaking of these crosslinks. Our study focuses on common composites that include Styrene Butadiene Rubber (SBR), EPDM and silica, but with the incorporation of devulcanized EPDM (EPDMd).

We have studied the mechanical, thermal, structural and dielectric properties of SBR composites containing EPDMd at variable compositions (0, 20, 40, 50, 60 phr). Employing techniques such as Thermogravimetric Analysis (TGA), Fourier Transform Infrared Spectropy (FTIR), and Scanning Electronic Microscopy (SEM), we have explored the microstructural changes driving the macroscopic effects on the measured properties.

The results show that incorporating EPDMd improves the crosslinking degree and, at optimal 40 phr loading, significantly increases the mechanical properties of SBR matrix. The addition of SiO₂, in general, reduce tensile strength and elongation, while increasing the Young's modulus, except for compositions around 40 phr EPDMd. The dielectric measurements are in concordance with the previous data, showing a moderation of the Maxwell–Wagner–Sillars (MWS) effect due to SiO2 in highly filled EPDMd composites at 40 phr EPDMd.

Estimating the operating conditions using conventional process analysis techniques for the maximum metal extraction from the wasted printed circuit boards (WPCB) can provide sub-optimal solutions leading to the low yield of the process. In this paper, we present a closed-loop methodological framework built on machine learning and robust mathematical optimization technique, that offers the mathematical rigour, to determine the optimum operating conditions for the maximum Cu and Ni recovery from the WPCB. Alkali leaching based novel metals recovery process from the WPCB is designed, and the experiments are conducted to collect the data on the percentage recovery of Cu and Ni against the operating levels of the process input variables (ammonia concentration (NH₃ conc. (g/L)), ammonium sulfate concentration ((NH₄)₂SO₄ conc. (g/L)), H₂O₂ concentration (H₂O₂ conc. (M)), time (h), liquid to solid ratio (L/S ratio, (mL/g)), temperature (Temp. (°C)), and stirring speed (rpm)). The experimental data is deployed to construct the functional mapping between the nonlinear output variables of metals recovery process with the hyperdimensional input space through artificial neural network (ANN) based modelling algorithm – a powerful universal function approximator. Well-predictive ANN models for Cu and Ni recovery are developed having co-efficient of determination (R²) value more than 0.90. Partial derivative-based sensitivity analysis is then carried out to establish the order of the significance of the input variables that is backed by the domain knowledge, thus promotes the interpretability of the trained ANN models. The hybridization of ANN with NLP (nonlinear programming) framework is implemented for the determination of optimized operating conditions to extract maximum Cu and Ni under separate and combined model of metal extraction. The robustness of the determined solutions is verified, the determined optimized solutions for the metal recovery are validated in the lab, and the maximum metal recovery, i.e., 100 % Cu and 90 % Ni is extracted from the WPCB. This research demonstrates the effective utilization of ANN model-based robust optimization approach for the metal recovery from the WPCB that supports the circular economy for the metal extraction industry.

The reduction of iron oxide-bearing ores necessitates the exploration of alternatives. Recycling iron oxide-enriched metallurgical dust could serve as secondary raw material for metallurgical processes. Implementing environmentally friendly technologies utilizing hydrogen has prompted the concept of hydrogen reduction of metallurgical dust to recycle secondary steel production products. The present study investigates the characteristics of hydrogen reduction of briquettes and pellets produced from basic oxygen furnace dust and reduced at the temperature of 850 °C. Experimental results revealed that the reduction degree for pellets was approximately 1.5 times higher compared to briquettes. The reduction swelling index of pellets was noticeable lower compared to literature data of reduction swelling index for iron ore pellets. Scanning electron microscopy/energy-dispersive X-ray spectroscopy was carried out to detect changes in the microstructure and chemical composition of the samples. Subsequent melting of the reduced samples unveiled non-metallic inclusions within the iron alloy and the impact of slag on their distribution between the alloy and slag.