Search Results (4,869)

This study presents a novel framework for city-level energy planning and retrofitting, tailored to Danish cities and neighborhoods. The framework addresses the challenges of large-scale urban energy modeling by integrating automated processes for data collection, energy demand prediction, and renewable energy integration. It combines open-source simulation tools and validated datasets, enabling efficient and scalable predictions of energy performance across urban areas, including streets, districts, and entire cities, with minimal user input. The key components include data collection and demand modeling, energy resource estimation, performance gap evaluation, and the design of retrofitting strategies with renewable energy integration. The DanCTPlan energy planning tool, developed based on this framework, was applied to two case studies in Denmark: a single street with 101 buildings and a district comprising five streets with 1284 buildings. In the single-street case, retrofitting all buildings to meet current regulations resulted in a 60.8% reduction in heat demand and a 5.8% reduction in electricity demand, with significant decreases in peak energy demands. The district-level retrofitting measures led to a 29.5% reduction in heat demand and a 2.4% reduction in electricity demand. Renewable energy scenarios demonstrated that photovoltaic systems supplying 30% of electricity demand and solar thermal systems meeting 10% of heating demand would require capacities of 2218 kW and 3540 kW, respectively. The framework’s predictive capabilities and flexibility position it as a robust tool to support decision-makers in developing sustainable and cost-effective energy strategies, paving the way toward establishing energy-efficient and positive energy districts. Full article

In light of global climate change and China’s commitment to carbon neutrality by 2060, this study explores energy-saving and decarbonization design optimization for educational buildings, with a specific focus on a high school canteen in Nanjing. Through a comparative analysis of optimal energy-saving and lifecycle decarbonization retrofit schemes, the study aims to identify the performance differences and provide practical guidance for retrofitting educational buildings. The optimization process involves two separate single-objective optimizations: one aimed at minimizing annual total primary energy consumption (TES) and the other at minimizing lifecycle carbon emissions (E). Energy performance is simulated using EnergyPlus 23.1.0, while the Strengthened Elitist Genetic Algorithm (SEGA) is applied to optimize design variables such as insulation materials, window types, window-to-wall ratios (WWRs), and photovoltaic (PV) system configurations. The results reveal that the optimal energy-saving scheme achieves zero net energy consumption annually, generating a surplus of 20,625.2 kWh (15.05 kWh/m²). Conversely, the optimal decarbonization scheme achieves zero lifecycle carbon emissions, contributing a carbon reduction of 386,926.4 kg, albeit with a 28.83% higher lifecycle TES compared to the energy-saving scheme. This study underscores the distinctions between energy-saving and decarbonization retrofits and offers valuable insights for sustainable retrofitting of educational buildings in China. Full article

The synthesis of nanoparticles (NPs) via laser ablation synthesis in solution (LASiS) is a promising method for sustainable and efficient nanoparticle fabrication. This work investigates the transition from one-factor-at-a-time experimentation to a more efficient, multivariate approach for optimising NP production efficiency. By applying the Industry 4.0 principles, the objective is to digitise and automate laboratory processes to increase productivity and robustness. Design of Experiments (DoE) strategies, Taguchi orthogonal arrays and full-factorial design (FFD), have been employed to enhance laser ablation processes. Both models confirmed that increasing laser power led to higher colloid absorbance, with the Taguchi DoE offering rapid initial process mapping and FFD providing a higher-resolution analysis. The optimal laser repetition rate of 30 kHz was identified as a balance between pulse energy and thermal effects on the target, maximising ablation efficiency. The Taguchi model had a prediction of NP size with an R² value of 0.49, while the FFD struggled with accurate size prediction. Additionally, this study introduced a recirculation flow regime as a rapid test platform for predicting optimal conditions for continuous flow production. Using a semi-autonomous DoE platform decreased the operator involvement and increased the process selectivity. This proof-of-concept for on-the-bench NP rapid manufacturing demonstrated how efficient NP synthesis processes can be developed by clarifying the effects of varying parameters on colloid productivity, paving the way for broader industrial applications in the future. Full article

Environmental, social, and governance (ESG) aspects have a growing relevance in the corporate world where the objective for sustainability becomes an essential point. The supply chain (SC) is a buyer’s responsibility and accounts for a large part of their ESG footprint. Since ESG performance extends to SC, poor ESG practices in the SC can negatively affect the sustainability of the Anchor Company (AC). Therefore, AC, the buyer, needs to go through a complex, expensive, and time-consuming process to assess their SC. The objective of this work is to develop an ESG assessment model for companies to receive a quantitative score of their footprint by considering both their operations and the SC. The model is verified by considering different scenarios that are designed by testing two different cases with different interactions between two ACs and two SCs with different ESG maturity levels. Results show that the SC has a significant impact on the final ESG score of the AC, highlighting the need for considering the SC to evolve in ESG aspects. In all tested cases, the SC accounted for more than 50% of the final consolidated ESG score. Despite differing ESG maturity levels, two ACs received the same consolidated score due to the influence of their SC scores. Results emphasize that achieving a strong consolidated ESG score is important, and advanced corporate sustainability is not possible without integrating the SC into the strategy. The novel methodology proposed contributes to sustainability, expanding the scope of ESG assessments to include SC and developing a standardized and adaptable model with practical applications. Full article

Coastal defense structures play a crucial role in mitigating wave impacts; yet, existing breakwater designs often face challenges in balancing wave reflection, energy dissipation, and structural stability. This study leverages machine learning (ML) to predict the optimal 2D dimensions of rectangular breakwaters in two configurations: submerged at the bottom of a wave tank and positioned at the free surface. Further, the objective is to achieve controlled wave reflection allowing a specific wave run-up and optimized energy dissipation, while ensuring maritime stability. Thus, we used an analytical equation modeling the reflection coefficient versus relative water depth (KH), for different immersion ratios of obstacle (h/H), and relative length (l/H). Two datasets of 32,000 data points were generated for underwater and free-surface breakwaters, with an additional 10,000 data points for validation, totaling 42,000 data points per case. Five ML algorithms—Random Forest, Support Vector Regression, Artificial Neural Network, Decision Tree, and Gaussian Process—were applied and evaluated. Results demonstrated that Random Forest and Decision Tree balanced accuracy with computational efficiency, while the Gaussian Process closely matched analytical results but demanded higher computational resources. These findings support ML as a powerful tool to optimize breakwater design, complementing traditional methods and contributing to more sustainable and resilient coastal defense systems. Full article

The growing need to mitigate the environmental impact of human activities has underscored the importance of biomaterials in sustainable architecture and construction. In this systematic review, advancements in bio-composite materials are consolidated and critically evaluated, emphasizing their thermal insulation properties and broader applications in sustainable building practices. Key aspects analyzed included morphology, internal structure, and thermal performance, along with supplementary insights into mechanical properties when available. The review focused on studies published between January and October 2024, sourced from the Scopus database and adhering to PRISMA guidelines. A keyword meta-analysis using VOSviewer (version 1.6.20) illustrated keyword co-occurrence trends. Methods for assessing bias included evaluating study design, data collection processes, and potential conflicts of interest, aligned with PRISMA standards. Significant findings revealed bio-composites achieving thermal conductivity values as low as 0.016 W/m·K, surpassing many traditional materials in insulation performance. Data from 48 studies, analysing 50 bio-composite materials, showed that 44% were optimized for thermal insulation and 40% for sub-structural applications. These materials also exhibit biodegradability and recyclability, critical attributes for sustainable construction. However, challenges such as scalability and durability remain as the key barriers to widespread adoption. In this review, the viability of bio-composites as sustainable alternatives to traditional materials is highlighted and research priorities are identified, particularly in scaling production technologies and enhancing durability testing methods, to advance their application in sustainable building practices. Full article

This study provides a comprehensive analysis of the composition of onion peels, tomato peels, and pistachio green hulls, with a focus on their structural and bioactive compounds. Onion peels, regardless of cultivar, were found to be rich in quercetin and its derivatives, along with other flavonoids and pectin. Tomato peels emerged as a notable source of naringenin (0.52 mg/g in ethanol extract) and rutin (0.24 mg/g in water extract) and showed an unexpectedly high lignin content, comprising nearly 50% of their structural components. Pistachio green hulls demonstrated a high extractive content (63.4 g/100 g), 73% of which were water-soluble. Protocatechuic acid, rutin, and quercetin derivatives were the dominant phenolic compounds in the water extract, while luteolin was most abundant in the ethanol extract. Regarding structural composition, tomato peels and pistachio green hulls shared similarities, exhibiting a high lignin content (53.4% and 33.8%, respectively) and uronic acids (10–15%). In contrast, onion peels were characterized by high levels of glucans (around 38%) and galacturonic acid (33%). The insights from this study pave the way for the design of sustainable and efficient extraction processes, enabling the sequential recovery of valuable bioactive compounds and promoting the valorization of these agro-industrial by-products. Additionally, onion and tomato peels were evaluated as sources of pectin using two extraction methods: conventional acid water extraction and subcritical water extraction. The results revealed significant differences in the pectin composition (53–68% galacturonic acid) and degree of esterification (79–92%) compared to commercial pectin (72.8% galacturonic acid and 68% esterification), highlighting the influence of the raw material and extraction method on the final properties of pectin. Full article

Plastics are indispensable to modern life, but their widespread use has created an environmental crisis due to inefficient waste management. Mixed plastic waste, comprising diverse polymers, presents significant recycling challenges due to the high costs of sorting and processing, leading to ecosystem accumulation and harmful by-product generation. This study addresses this issue by engineering a synthetic bacterial consortium (SBC) designed to degrade mixed plastic monomers. The consortium pairs Escherichia coli Nissle 1917, which uses ethylene glycol (EG), a monomer derived from polyethylene terephthalate (PET), as a carbon source, with Pseudomonas putida KT2440, which metabolizes hexamethylenediamine (HD), a monomer from nylon-6,6, as a nitrogen source. Adaptive evolution of the SBC revealed a novel metabolic interaction where P. putida developed the ability to degrade both EG and HD, while E. coli played a critical role in degrading glycolate, mitigating its by-product toxicity. The evolved cross-feeding pattern enhanced biomass production, metabolic efficiency, and community stability compared to monocultures. The consortium’s performance was validated through flux balance analysis (FBA), high-performance liquid chromatography (HPLC), and growth assays. These findings highlight the potential of cross-feeding SBCs in addressing complex plastic waste, offering a promising avenue for sustainable bioremediation and advancing future polymer degradation strategies. Full article

The sustainability evaluation of engineering processes and structures is a multifaceted challenge requiring the integration of diverse and often conflicting criteria. To address this challenge, Multi-Criteria Decision-Making (MCDM) methods have emerged as effective tools. However, the selection of the most suitable MCDM approach for problems involving multiple criteria is critical to ensuring robust, reliable, and actionable outcomes. Equally significant is the choice of a proper normalization technique, which plays a pivotal role in determining the robustness and reliability of the results. This study investigates the impact of common MCDM tools on the decision-making process concerning diverse aspects of sustainability. It also examines how different normalization methods influence the final outcomes. Sustainability in this context is understood as a trade-off among five key dimensions: performance, environmental impact, economic impact, social impact, and circularity. The outcome of the MCDM process is represented by an aggregated metric, referred to as the Sustainability Index (SI). This index offers a comprehensive and robust framework for evaluating sustainability and facilitating decision-making when conflicting criteria are present. To assess the effects of implementing different MCDM and normalization choices on the sustainability assessment, a dataset from the aviation sector is employed. Specifically, a typical aircraft component is analyzed as a case study for holistic sustainability assessment, utilizing data that represent the various dimensions of sustainability mentioned above, for this component. Additionally, the study investigates the influence of initial data variations and weight variations within the MCDM process on the results. The results indicate that, overall, the different MCDM and normalization methods lead to similar outcomes when applied to the design alternatives. However, a deeper dive into the results reveals that the weighted sum method, when paired with min-max normalization, appears to be more appropriate, based on the use case involved for the present investigation, due to its robustness regarding small variations in the initial data and its sensitivity to large ones. This research underscores the critical importance of selecting appropriate MCDM tools and normalization methods to enhance transparency, robustness, reliability, and consistency of sustainability assessments within a holistic framework. Full article

This paper proposes an efficient cloud detection algorithm for Sustainable Development Scientific Satellite (SDGSAT-1) data. The core work includes the following: (1) constructing a SDGSAT-1 cloud detection dataset containing five types of elements: clouds, cloud shadow, snow, water body, and land, with a total of 15,000 samples; (2) designing a multi-scale convolutional attention unit (RDE-MSCA) based on a gated linear unit (GLU), with parallel re-parameterized convolution (RepConv) and detail-enhanced convolution (DEConv). This design focuses on improving the feature representation and edge detail capture capabilities of targets such as clouds, cloud shadow, and snow. Specifically, the RepConv branch focuses on learning a new global representation, reconstructing the original multi-branch deep convolution into a single-branch structure that can efficiently fuse channel features, reducing computational and memory overhead. The DEConv branch, on the other hand, uses differential convolution to enhance the extraction of high-frequency information, and is equivalent to a normal convolution in the form of re-parameterization during the inference stage without additional overhead; GLU then realizes adaptive channel-level information regulation during the multi-branch fusion process, which further enhances the model’s discriminative power for easily confused objects. It is integrated into the SegNeXt architecture based on RDE-MSCA and proposed as RDE-SegNeXt. Experiments show that this model can achieve 71.85% mIoU on the SDGSAT-1 dataset with only about 1/12 the computational complexity of the Swin-L model (a 2.71% improvement over Swin-L and a 5.26% improvement over the benchmark SegNeXt-T). It also significantly improves the detection of clouds, cloud shadow, and snow. It achieved competitive results on both the 38-Cloud and LoveDA public datasets, verifying its effectiveness and versatility. Full article

The kinetic study of micro-electrolysis biotechnology not only determines the removal efficiency of a micro-electrolysis process but also influences the optimal design of a system. This paper investigates the relationship between electric field strength, pollutant degradation rate, and biofilm thickness by constructing a microporous biofilm model for pollutant removal. Additionally, the study derives equations linking electric field strength to reaction rate, pollutant effluent concentration, and biofilm thickness under both high and low pollutant influent concentrations. This work bridges the gap between macroscopic processes and periplasmic mechanisms, enhancing our understanding of pollutant removal mechanisms and facilitating process optimization. It also provides theoretical support for the sustainable development of micro-electrolysis biotechnology. Future research will focus on experimental validation and the optimization of model accuracy and flexibility to accommodate diverse treatment conditions. Full article

The Critical Raw Materials Act (CRMA), enacted by the European Union (EU) in May 2024, represents a strategic framework that aims to address the growing demand for critical raw materials (CRMs) and reduce dependency on non-EU sources. The present study explores the potential of CRM recovery from granite extractive waste (EW) at a granite quarry in Buddusò (Sardinia, Italy). A significant quantity of granite EW, stored in piles within designated disposal areas at the quarry under study, is estimated in terms of mass and volume using GISs and digital elevation models (DEMs). Analysis performed using a scanning electron microscope attached to an energy-dispersive spectrometer (SEM-EDS) reveals the presence of allanite, a rare-earth-bearing mineral with substantial light rare-earth elements (LREEs), which can potentially be exploited for LREE recovery. A combined working process including gravitational and magnetic separations yields CRM-enriched fractions with concentrations of REEs, Sc, and Ga, reaching levels of potential economic interest for different industrial applications. Despite promising concentrations, limited knowledge of allanite processing represents significant challenges for CRM recovery from this waste. Therefore, the present study was conducted to assess the efficiency of these gravitational and magnetic separation methods in order to concentrate CRMs from granite EW. Economic evaluations, including potential market value estimates, suggest that CRM recovery from granite EW can be very profitable under optimized processing conditions. Expanding studies to other quarries in the region can provide valuable insights into the feasibility of establishing a recycling hub, offering a sustainable supply chain solution for CRMs within the EU’s strategic framework. Full article

This paper aims to explore both experimental and modeling anaerobic digestion (AD) processes as innovative methods for managing the substantial quantities of spent coffee grounds (SCG) generated in Algeria, transforming them into valuable renewable energy sources (biogas/methane). AD of SCG, while promising, is hindered by its complex lignocellulosic structure, which poses a significant challenge. This study investigates the efficacy of hydrogen peroxide (H₂O₂) pretreatment in addressing this issue, with a particular focus on enhancing biogas and methane production. The AD of SCG was conducted over a 46-day period, and the impact of H₂O₂ pretreatment was evaluated using laboratory-scale batch anaerobic reactors. Four different concentrations of H₂O₂ (0.5, 1, 2, and 4% H₂O₂ w/w) were studied in mesophilic conditions (37 ± 2) for 24 h at room temperature, providing basic data on biogas and methane production. The results showed a significant increase in soluble oxygen demand (SCOD) and total sugar solubilization in the range of 555.96–713.02% and 748.48–817.75%, respectively. The optimal pretreatment was found to be 4% H₂O₂ w/w resulting in 16.28% and 16.93% improvements in biogas and methane yield over the untreated SCG. Further, while previous research has established oxidative pretreatment efficacy, this study uniquely combines the empirical analysis of H₂O₂ pretreatment with a detailed kinetic modeling approach using the modified Gompertz (MG) and logistic function (LF) models to estimate kinetic parameters and determine the accuracy of fit. The MG model showed the most accurate prediction, thus making the present investigation a contribution to understanding the performance of the AD system under oxidative pretreatment and designing and scaling up new systems with predictability. These findings highlight the potential of H₂O₂-pretreated SCG as a more efficient and readily available resource for sustainable waste management and renewable energy production. Full article

In the adaptive re-use of buildings, the physicality of buildings—the way they are designed, planned, constructed and maintained—has fallen out of fashion in favour of socio-economic conceptualisations and critical urban interpretations of the redevelopment process. However, the materiality of buildings plays a key part in how locations are re-produced in response to socio-economic circumstances—in this case, the creation and sustaining of cultural and creative clusters. In response, this paper adopts a forensic approach to the characteristics of physical buildings in order to develop an original taxonomy of lo-fi adaptive features and interventions that enable the authors to infer which types and aspects of industrial buildings lend themselves to sustaining cultural and creative clusters. The focus on lo-fi interventions is an original contribution to the adaptive re-use literature where attention tends to focus on more formal and traditional design-based interactions with existing buildings. In doing so, the research utilises a comparative case study approach of several former industrial buildings associated with the contemporary independent food and drink industry in the Ouseburn Valley cultural and creative quarter of Newcastle upon-Tyne in England. The research finds that it is the functional tolerance and malleability of the case study buildings—their inherent adaptive capacity, that in part helps to sustain the cultural and creative cluster in this location. Full article

Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities. Full article