Search Results (237)

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

This paper analyzes the concept of packaging redesign, with the primary objective of improving material utilization. It further examines the potential environmental and economic benefits that may result from effective packaging redesign for both industry and society. The research is based on a specific case study comparing two alternative bottle designs with identical capacity, focusing on shape, material usage, and space efficiency. A detailed numerical comparison highlights the advantages and disadvantages of each option. The analysis demonstrates that an optimized bottle design can lead to substantial material savings and waste reduction. For example, an 8% reduction in bottle weight could eliminate approximately 1.6 million tons of material annually, potentially translating into economic savings exceeding 3 billion U.S. dollars per year. The study underscores how strategic packaging redesign can yield significant benefits in terms of material efficiency and cost savings for companies. It also contributes to the field of Life Cycle Analysis by linking packaging design innovation to key environmental and economic outcomes, while ensuring that packaging continues to protect products and meet the needs of the end consumer. Full article

Autoclaved Aerated Concrete (AAC) is a lightweight, thermally insulating, and fire-resistant building material that has become prominent in sustainable construction due to its reduced production energy demands and minimal environmental impact. As an increasing number of AAC-based structures reach end-of-life, the effective recycling and reuse of AAC waste present both challenges and opportunities within the context of sustainable building practices and circular economy frameworks. This study presents a scientometric review of AAC recycling research published between 2014 and 2024, using the Web of Science database and bibliometric tools such as CiteSpace. Key trends, techniques, and knowledge gaps in AAC recycling are identified, highlighting issues such as high energy consumption, limited practical implementation, and the absence of standardized recovery protocols. The study also outlines emerging research pathways, including detailed material characterization, development of recycling standards, innovative reuse techniques, hybrid material systems, and the integration of recycled AAC in new construction. These insights provide a foundation for advancing sustainable building material strategies and inform policy and practice in construction waste management. Full article

Ceramic tiles classified as non-biodegradable are made from fired clay, silica, and other natural materials for several construction applications. Waste ceramic tiles (WCTs) are produced from several sources, including manufacturing defects; surplus, broken, or damaged tiles resulting from handling; and construction and demolition debris. WCTs do not decompose easily, leading to long-term accumulation in landfills and occupying a significant amount of landfill space, which has substantial environmental impacts. Recycling WCTs offers several critical ecological benefits, including reducing landfill waste and pollution, conserving natural resources, lowering energy consumption, and supporting the circular economy, which in turn contributes to sustainable construction and waste management practices. In green concrete manufacturing, WCTs are widely utilized as replacements for cement, fine, and coarse aggregates, and the recycling level in the concrete industry is an increasingly explored practice aimed at promoting sustainability and reducing construction waste. From this view, this paper reports the innovative technologies, advancements in green concrete performance, and development trends in the reuse of WCTs in the production of systems. The effects of WCTs on fresh, engineering, microstructural, and durable properties, as well as their environmental performance, are reviewed. In conclusion, the use of technologies for recycling WCTs has demonstrated potential in promoting sustainability and supporting the transition toward a more environmentally friendly construction industry. This approach offers a practical contribution to sustainable development and represents significant progress in closing the recycling loop within the construction sector. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article

Accurate preform design in forging processes is critical for improving part quality, conserving material, reducing manufacturing costs, and eliminating secondary operations. This paper presents a finite element (FE) simulation-based methodology for preform design aimed at achieving flashless and near-flashless forging. The approach leverages material point backtracking within FE models to generate physics-informed preform geometries that capture complex material flow, die geometry interactions, and thermal gradients. An iterative scheme combining backtracking, surface reconstruction, and point-cloud solid modeling was developed and applied to several three-dimensional forging case studies, including a cross-joint and a three-lobe drive hub. The methodology demonstrated significant reductions in flash formation, particularly in parts that traditionally exhibit severe flash under conventional forging. Beyond supporting the development of new flashless forging sequences, the method also offers a framework for modifying preforms during production to minimize waste and for diagnosing preform defects linked to variability in frictional conditions, die temperatures, or material properties. Future integration of the proposed method with design of experiments (DOE) and surrogate modeling techniques could further enhance its applicability by optimizing preform designs within a localized design space. The findings suggest that this approach provides a practical and powerful tool for advancing both new and existing forging production lines toward higher efficiency and sustainability. Full article

The use of recycled aggregate concrete (RAC) in construction mitigates environmental pollution by repurposing demolition waste, but its lower compressive strength compared to natural aggregate concrete (NAC) limits broader application. Although carbon fiber reinforced polymer (CFRP) composites and polyvinyl chloride (PVC) tubes have individually been shown to improve concrete strength and ductility, existing studies focus on fully wrapped CFRP jackets on NAC columns and do not systematically explore CFRP–PVC hybrid confinement using strips on RAC. To address this research gap, this study investigates the axial compressive behavior of CFRP–PVC–RAC columns by varying CFRP strip width (from 25 to 75 mm), strip spacing (from 31 to 77.5 mm), and the number of CFRP layers (one to nine) over a central PVC tube. Axial compression tests reveal that specimens with a central CFRP strip width equal to or greater than 75 mm achieve peak loads up to 1331 kN and that, after rupture of the central strip, the remaining strips continue to carry load, producing a more gradual stress–strain decline and enhanced ductility compared to fully wrapped controls (peak load 1219 kN). These results show that CFRP–PVC composites enhance the axial compressive strength and ductility of RAC columns. The confinement mechanism increases the ultimate axial strain and redistributes transverse stresses, delaying brittle failure and improving deformation capacity. When two or more CFRP layers are applied, strip width and spacing affect axial stress by no more than three percent. Increasing layers from one to four raises axial strength by approximately 23 percent, whereas adding layers beyond four yields diminishing returns, with less than a six percent increase. Finally, a multilayer lateral confined pressure formula is derived and validated against thirty-two specimens, exhibiting errors no greater than three percent and accurately predicting effective confinement. These findings offer practical guidance for optimizing strip dimensions and layering in CFRP–PVC reinforcement of RAC columns, achieving material savings without compromising performance. Full article

Fused filament fabrication (FFF) is a 3D printing technology that has been successfully demonstrated aboard the International Space Station (ISS), proving its suitability for space applications. In this study, we aimed to apply FFF to the 3D printing of recycled space beverage packaging, made of LDPE and a PET-Aluminum-LDPE (PAL) trilaminate. To minimize material waste and optimize the experimental process, we first conducted numerical simulations of additive manufacturing. Using Digimat-AM 2021.1 software, we analyzed residual stresses and warpage in an LDPE/PAL composite with a 10 wt% filler content, processed through the FFF technique. Three key printing parameters, including printing speed and infill pattern, were varied across different levels to assess their impact. Once the optimal combination of parameters for minimizing residual stresses and warpage was identified, we proceeded with the experimental phase, printing objects of increasing complexity to validate the correlation between numerical predictions and the 3D-printed models. The successful fabrication of all geometries under optimized conditions confirmed the numerical predictions, particularly the reduction in warpage and residual stress, validating the material’s viability for additive manufacturing. These findings support the potential application of the LDPE/PAL composite for in situ resource utilization strategies in long-term space missions. Full article

This study reviews life cycle assessments (LCAs) of reprocessed single-use devices (rSUDs) in healthcare to quantify their greenhouse gas (GHG) emission reductions compared to original equipment manufacturer (OEM) SUDs (single-use devices). rSUDs offer notable reductions in solid waste generation, but, until recently, a reduction in greenhouse gases and other emissions from the reprocessing process was only hypothesized. Emerging LCAs in this space can help validate the assumptions of better environmental performance from greater circularity in the medical device industry. Four LCAs analyzing eight devices found consistent and significant GHG reductions ranging from 23% to 60% with rSUD use. Primary data from rSUD manufacturers were utilized in all studies, with SimaPro v9.3.0.2 and Ecoinvent v3.8 being the predominant LCA software and database. Raw material extraction and production dominated SUD emissions, while electricity use and packaging materials were key contributors for rSUDs. Sensitivity analyses highlighted the influence of electricity sources, collection rates, and reprocessing yields on rSUD environmental performance. A comparison with economic input–output-based models revealed an alignment at the time between price differentials and LCA-derived GHG differences, though this may not always hold true. This review demonstrates the substantial environmental benefits of rSUDs, supporting their role as a readily achievable step towards more sustainable and circular healthcare systems. Full article

Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice. Full article

In this research work, two distinct types of three-dimensional (3D) capacitors were successfully fabricated, each with its own unique features and advantages. The first type of capacitor is centered around a 3D nanoporous structure. This structure is formed on a nickel substrate through anodic oxidation. After undergoing high-temperature thermal oxidation, a monolithic Ni-NiO-Pt metal–insulator–metal (MIM) capacitor with a nanoporous dielectric architecture is achieved. Structurally, this innovative design brings about several remarkable benefits. Due to the nanoporous structure, it has a significantly increased surface area, which can effectively store more charges. As a result, it exhibits an equivalent capacitance density of 69.95 nF/cm², which is approximately 18 times higher than that of its planar, non-porous counterpart. This high capacitance density enables it to store more electrical energy in a given volume, making it highly suitable for applications where miniaturization and high energy storage in a small space is crucial. The second type of capacitor makes use of Through-Glass Via (TGV) technology. This technology is employed to create an interdigitated blind-via array within a glass substrate, attaining an impressively high aspect ratio of 22.5:1 (with a via diameter of 20 μm and a depth of 450 μm). By integrating atomic layer deposition (ALD), a conformal interdigital electrode structure is realized. Glass, as a key material in this capacitor, has outstanding insulating properties. This characteristic endows the capacitor with a high breakdown field strength exceeding 8.2 MV/cm, corresponding to a withstand voltage of 5000 V. High breakdown field strength and withstand voltage mean that the capacitor can handle high-voltage applications without breaking down easily, which is essential for power-intensive systems like high-voltage power supplies and some high-power pulse-generating equipment. Moreover, due to the low-loss property of glass, the capacitor can achieve an energy conversion efficiency of up to 95%. Such a high energy conversion efficiency ensures that less energy is wasted during the charge–discharge process, which is highly beneficial for energy-saving applications and systems that require high-efficiency energy utilization. Full article

This study aims to address the challenge of backfill compaction in the confined spaces of municipal utility tunnel trenches and to develop an environmentally friendly, zero-cement-based backfill material. The research focuses on the excavation slag soil from a utility tunnel project in Handan. An alkali-activated industrial-solid-waste-excavated slag-soil-based controllable low-strength material (CLSM) was developed, using NaOH as the activator, a slag–fly ash composite system as the binder, and steel slag-excavated slag as the fine aggregate. The effects of the water-to-solid ratio (0.40–0.45) and the binder-to-sand ratio (0.20–0.40) on CLSM fluidity were studied to determine optimal values for these parameters. Additionally, the influence of excavated soil content (45–65%), slag content (30–70%), and NaOH content (1–5%) on fluidity (flowability and bleeding rate) and mechanical properties (3-day, 7-day, and 28-day unconfined compressive strength (UCS)) was investigated. The results showed that when the water-to-solid ratio is 0.445 and the binder-to-sand ratio is 0.30, the material meets both experimental and practical requirements. CLSM fluidity was mainly influenced by the excavated soil and slag contents, while NaOH content had minimal effect. The unconfined compressive strength at different curing ages was negatively correlated with the excavated soil content, while it was positively correlated with slag and NaOH content. Based on these findings, the preparation of “zero-cement” CLSM using industrial solid waste and excavation slag is feasible. For trench backfill projects, a mix of 50–60% excavated soil, 40–60% slag, and 3–5% NaOH is recommended for optimal engineering performance. CLSM is a new type of green backfill material that uses excavated soil and industrial solid waste to prepare alkali-activated materials. It can effectively increase the amount of excavated soil and alleviate energy consumption. This is conducive to the reuse of resources, environmental protection, and sustainable development. Full article

The incorporation of waste ground glass powder (GGP) in concrete as a partial replacement of cement offers significant environmental benefits, such as reduction in CO₂ emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a heterogeneous material, and the prediction of its accurate compressive strength is challenging due to the inclusion of several non-linear parameters. This study explores the utilization of different machine learning (ML) algorithms: linear regression (LR), ElasticNet regression (ENR), a K-Nearest Neighbor regressor (KNN), a decision tree regressor (DT), a random forest regressor (RF), and a support vector regressor (SVR). A total of 187 sets of pertinent mix design experimental data were collected to train and test the ML algorithms. Concrete mix components such as cement content, coarse and fine aggregates, the water–cement ratio (W/C), various GGP chemical properties, and the curing time were set as input data (X), while the compressive strength was set as the output data (Y). Hyperparameter tuning was carried out to optimize the ML models, and the results were compared with the help of the coefficient of determination (R²) and root mean square error (RMSE). Among the algorithms considered, SVR demonstrates the highest accuracy and predictive capability with an R² value of 0.95 and RMSE of 3.40 MPa. Additionally, all the models exhibit R² values greater than 0.8, suggesting that ML models provide highly accurate and cost-effective means for evaluating and optimizing the compressive strength of GGP-incorporated sustainable concrete. Full article

The purification of waste cooking oils (WCOs) through clay-based adsorption is an established recycling method, yet the relationship between clay composition and adsorption efficiency remains an area of active research. The aim of the present research work was to assess the performance of Maghnia bentonite in WCO decoloration and to gain information about the specific refining process. Thus, natural bentonite from the Maghnia region (Algeria) was investigated as an adsorbent for WCO refining for biolubricant production. The adsorption efficiency was evaluated under different conditions, achieving up to 70% decolorization at 10 wt% clay after 4 h of treatment. Structural characterization of the bentonite before and after adsorption was conducted using FT-IR spectroscopy, powder X-ray diffraction (XRD), and X-ray fluorescence (XRF) to assess compositional and morphological changes. FT-IR analysis confirmed the adsorption of organic compounds, XRD indicated minor alterations in interlayer spacing, and XRF revealed ion exchange mechanisms, including a reduction in sodium and magnesium and an increase in calcium and potassium. Adsorption kinetics followed a pseudo-second-order model, with desorption effects observed at prolonged contact times. The pHPZC of 8.3 suggested that bentonite adsorption efficiency is enhanced under acidic conditions. The high decoloration capacity of Maghnia bentonite, combined with the availability and the low cost of the material, suggests a possible industrial application of this material for WCO refinement, especially in lubricant production. Full article

The extensive use of sodium-ion batteries has made it important to develop high-performance anode materials. Owing to their good sustainability, low cost, and excellent electrochemical properties, hard carbon materials are expected to be a good choice, especially biomass-derived hard carbon. In this study, we successfully synthesized a coir-based carbon nanosphere as an anode material. The hard carbon has a low degree of structural ordering, small particle size, and multiple pore networks for easy sulfur doping compared to the conventional direct high-temperature sulfur doping. The material has a high reversible capacity of 536 mAh g⁻¹ and an initial Coulombic efficiency of 53%, maintaining a reversible capacity of 308 mAh g⁻¹ at a high current density of 5 A g⁻¹, achieving a capacity retention of 90.3% after 1000 cycles. The performance enhancement stems from a combination of enlarged layer spacing, an increased specific surface area, enhanced porosity, and doped sulfur atoms. This study provides an effective strategy for the conversion of biomass waste into high-performance sodium-ion anode material batteries. Full article