Environmental Efficiency Research Articles

Recent progress in green thin film membrane based materials for desalination: Design, properties and applications

The urgent need for sustainable solutions to address freshwater scarcity has encouraged significant research interest in green thin film membrane-based materials (GTFM) for desalination applications. This review paper comprehensively examines recent progress in these innovative membranes' design, properties, and applications. Emphasizing environmental sustainability and efficiency, various fabrication techniques such as electrospinning, layer-by-layer assembly, and template synthesis are discussed for their role in tailoring membrane structures at the nanoscale. The review highlights the importance of incorporating eco-friendly materials, such as biopolymers, natural extracts, and nanocellulose, in membrane formulations to enhance biodegradability and reduce environmental impact. Furthermore, the paper investigates the crucial properties of green film membranes, including permeability, selectivity, mechanical strength, and fouling resistance. Additionally, the review explores recent advancements in functionalization strategies, such as surface modification and incorporation of nanomaterials, to enhance membrane properties and address specific desalination challenges. The applications of green thin film membranes across various desalination technologies, including reverse osmosis, nanofiltration, and forward osmosis, are thoroughly examined. Moreover, the review discusses emerging trends and future directions in green thin film membrane research, such as multifunctional membranes, hybrid systems, and integrated desalination processes. In summary, this review provides valuable insights into the design principles, fundamental properties, and diverse applications of GTFM-based materials for sustainable desalination, paving the way for the development of next-generation water purification technologies.

EffiMultiOrthoBearNet: An Efficient Lightweight Architecture for Bearing Fault Diagnosis

Amidst the advent of Industry 4.0 and the rapid advancements in smart manufacturing, the imperative for developing resource-efficient condition monitoring and fault prediction technologies tailored for industrial equipment in resource-limited settings has become increasingly evident. This study puts forward EffiMultiOrthoBearNet, an innovative, lightweight, deep learning model specifically designed for the accurate identification and classification of bearing faults. Central to EffiMultiOrthoBearNet’s architecture is the integration of multi-scale convolutional layers and orthogonal attention mechanisms—key innovations that significantly enhance the model’s performance. Leveraging advanced feature extraction capabilities, EffiMultiOrthoBearNet meticulously processes Continuous Wavelet Transform (CWT) images from the CWRU dataset, ensuring the precise delineation of essential bearing signal traits through its multi-scale and attention-enhanced mechanisms. Optimized for supreme operational efficiency in resource-deprived environments, EffiMultiOrthoBearNet achieves unmatched classification accuracy—up to 100% under ideal circumstances and consistently above 90% amidst significant noise and operational complexities. Demonstrating remarkable adaptability and efficiency, EffiMultiOrthoBearNet provides a pioneering and practical fault diagnosis solution for industrial machinery across a wide range of application scenarios, even under stringent resource limitations.

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

Does larger scale enhance carbon efficiency? Assessing the impact of corporate size on manufacturing carbon emission efficiency

This study investigates the impact of corporate scale on manufacturing corporate carbon efficiency (MCEE) using empirical analyses and diverse modeling techniques. The research begins with rigorous unit root and cointegration tests, confirming the stationary nature of the data and establishing long-term equilibrium relationships among the variables. Subsequently, benchmark regression analyses employing various models, including system GMM, reveal a robust and significantly positive association between corporate scale and MCEE. The findings emphasize that as corporate scale increases, there is a substantial enhancement in MCEE, attributed to expanded resources, technological advancements, and improved management systems. Robustness tests employing alternative approaches consistently reaffirm this positive correlation, further validating the pivotal role of corporate scale in augmenting MCEE. Furthermore, nonlinear analyses demonstrate varying impacts across different quantile levels of MCEE, highlighting the consistent positive correlation between corporate scale and MCEE. Additionally, the panel threshold model underscores the influence of environmental regulations, R&D investments, and trade openness on this relationship. Notably, stricter environmental regulations intensify the impact of corporate scale on MCEE, emphasizing the importance of corporate expansion in minimizing environmental costs and enhancing resource efficiency. These findings underscore the significance of corporate scale in driving corporate carbon efficiency. They advocate for corporations to not only expand their production capacities but also focus on optimizing management strategies and resource allocation, particularly in contexts influenced by environmental regulations, R&D investments, and trade openness. Overall, this research contributes comprehensive insights into understanding the dynamics governing corporate environmental efficiency and emphasizes the critical role of corporate scale in fostering environmental sustainability.

Experimental analysis of dust's impact on solar photovoltaic system efficiency in arid environments: a case study in Southern Algeria.

The purpose of this study is to explore the effects of accumulated dust and weather conditions on the energy generated by solar photovoltaic panels in Ouargla, Algeria, between May 3 and August 3, 2023. For this experiment, two monocrystalline panels with a power output of 390 W manufactured by Zergoune Green Energy Company, as well as data-logging equipment, were used. The first panel was perfectly cleaned before starting every test and the second panel remained uncleaned. On day 90, the cleaned panel maintained an average power of 193 W, while the dusty panel exhibited a lower average power of 139 W. The greatest average reduction in efficiency, approximately 36.32%, occurred after 3months of exposure to weather conditions. The scanning electron microscope (SEM) analysis demonstrates the existence of microscopic dust particles which prevent part of solar radiation away instead of being absorbed by the photovoltaic cells, leading to a drop in the efficiency of the PV module. The primary chemical elements found in dust are oxygen, silicon, aluminum, and magnesium.

Research on multi-UUV mission planning for mine countermeasures based on preferred multi-objective optimization

Facing rising maritime security threats, this study presents T-MOEA/D, a sophisticated evolutionary algorithm enhancing UUVs’ capabilities in mine detection and neutralization. The algorithm tackles the multi-objective challenge by balancing time and energy, integrating user preferences into its optimization process. It leverages genetic operators such as dual chromosome encoding and partially mapped crossover to evolve efficient solutions, outperforming T-NSGA-II and T-NSGA-III in hypervolume and operational time. The UUV, directed by T-MOEA/D, navigates to operational areas and employs StyleGAN and YOLOv9 for accurate mine perception, crucial for executing mine countermeasure tasks. The system’s effectiveness is confirmed through Unity3D simulations and real-world tests, demonstrating its practicality and reliability. The study’s findings offer strategic guidance for planning large-scale mine countermeasure missions with multiple UUVs, ensuring operational efficiency and safety in complex underwater environments. The Pareto optimal solutions align with user preferences, reflecting a tailored approach to mine countermeasure missions.

Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance

At ByteDance, where we execute over a million Spark jobs and handle 500PB of shuffled data daily, ensuring resource efficiency is paramount for cost savings. However, achieving optimization of resource efficiency in large-scale production environments poses significant challenges. Drawing from our practical experiences, we have identified three key issues critical to addressing resource efficiency in real-world production settings: 1 slow I/Os leading to excessive CPU and memory idleness, 2 coarse-grained resource control causing wastage, and 3 sub-optimal job configurations resulting in low utilization. To tackle these issues, we propose a resource efficiency governance framework for Spark workloads. Specifically, 1 we devise the multi-mechanism shuffle services, including Enhanced External Shuffle Service (ESS) and Cloud Shuffle Service (CSS), where CSS employs a push-based approach to enhance I/O efficiency through sequential reading. 2 We modify the Spark configuration parameter protocol, allowing for fine-grained resource control by introducing several new parameters such as milliCores and memoryBurst, as well as supporting operators with additional spill modes. 3 We design a two-stage configuration autotuning method, comprising rule-based and algorithm-based tuning, providing more reliable Spark configuration optimizations. By deploying these techniques on millions of Spark jobs in production over the last two years, we have achieved over 22% CPU utilization increase, 5% memory utilization increase, and 10% shuffle block time ratio decrease, effectively saving millions of CPU cores and petabytes of memory daily.

Evaluating environmental and economic efficiency of a super-intensive Vannamei shrimp farm for nutrient flow circulation

Abstract Shrimp farming has provided several significant economic benefits to the residents of the Mekong Delta region. However, it posed numerous environmental concerns due to high nutrient content in wastes. Therefore, the purpose of this study is to assess the nutrient recovery efficiency from a super-intensive shrimp pond system for nutrient circulation. The material flow analysis tool was used to analyze the effectiveness of allocating nutrients (nitrogen, phosphorus, and carbon) in the research area’s super-intensive Vannamei shrimp farming system, and then construct a material flow circulating system. The findings revealed that in the current system, the amount of nutrients that shrimp absorb is 26.3% TN, 12.2% TP and 17.5% TOC. This study designed a system using circulating fluxes of nutrients. This system combines settling ponds and biological sedimentation ponds as well as composting and biogas digester solutions. The proposed system also reduces input nutrient costs by 53.6% TN, 62.8% TP and 40% TOC. On the other hand, the proposed system will meet the amount of heat for cooking for the 3 households and generate 1 ton of compost/crop for the garden.

High covertness camouflage covert underwater acoustic communication based on masking technique

In marine military operations, it is very important to ensure the safe transmission of underwater information, so it is urgent to improve the covertness of underwater communication. In order to meet the requirement of high covertness in underwater acoustic communication, according to the natural characteristics of superimposed interference of marine ambient noise, a high covertness camouflage covert underwater acoustic communication (CUWAC) method based on masking technique using ship-radiated noise as communication carrier is proposed in this paper. This method leverages innovative audio digital technology to embed information into the DCT domain of ship-radiated noise, creating a disguised signal that is hard to detect, thereby significantly enhancing stealth without compromising communication effectiveness. In order to improve the demodulation performance of this camouflage CUWAC, this paper proposes a variable step size p-norm-like LMP sparse adaptive filtering algorithm to offset the interference of the masking signal at the receiving end. This adaptive filtering algorithm is based on the improved modified Gaussian function to construct step size. Furthermore, it introduces the error signal autocorrelation function to adjust the step size and zero-attracting term dynamically. The proposed adaptive filtering algorithm obtains excellent anti-noise performance. Through a series of simulation experiments, this paper not only validates the feasibility of the CUWAC method but also demonstrates its stability and reliability under various marine environmental conditions, providing robust theoretical support for practical applications, proving its efficiency and reliability in complex marine environments.

Root and rhizosphere traits for enhanced water and nutrients uptake efficiency in dynamic environments.

Modern agriculture's goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation.

Optimisation of the Circular Economy Based on the Resource Circulation Equation

The lack of effective evaluation methods and implementation guidelines has led to frequent obstacles in the process of circular economy in enterprises. The efficiency equation for resource circulation can effectively evaluate the efficiency of an enterprise’s circular economy resource circulation from three perspectives: input, circulation, and output. Additionally, it delves into each link to identify weak points, offering guidance for optimising the enterprise’s circular economy. Utilising a value flow analysis within the context of a circular economy, this paper evaluates circular economy efficiency using a resource circulation efficiency equation. It conducts factor analysis across three dimensions: resource input, resource circulation, and waste output. This analysis aims to evaluate the corresponding resource productivity, added value output rate, and environmental efficiency. Factor decomposition techniques were then employed to identify the underlying factors contributing to poor circular economy outcomes. Furthermore, based on the relationships among three resource circulation indicators, this paper forecasts the potential advantages of integrating circular economy improvement measures and proposes practical optimisation approaches. The enhanced resource circulation efficiency resulting from the proposed optimisation approaches was validated through a case study with an aluminium company.

Salmon lice biology, environmental factors, and smolt behaviour with implications for the Norwegian salmon farming management system: A critical review

AbstractIn 2017, a new regulatory management system, the traffic light system (TLS), was implemented to estimate the effects of salmon lice from Norwegian salmon aquaculture on marine survival of wild Atlantic salmon and forms the basis of aquaculture capacity regulation. The TLS relies on observational data and a set of models to estimate the risk for negative impact on wild salmon populations. This review of the literature that forms the basis for the TLS as well as other relevant studies is presented in the context of the currently practiced TLS and suggestions are made for immediate and long‐term improvements. The main findings of this review are that: (1) assumed timing and duration of smolt migration contribute to unreliable observational and modelled data and overestimates of infection pressure; (2) production of lice larvae from farmed salmon is overestimated; (3) TLS model systems rely on or are calibrated by the same potentially flawed data; (4) lice‐associated mortality in wild salmon smolts may be overestimated; and (5) lice infection levels on farms are not associated with measurable effects on wild salmon. Recommendations to improve the accuracy and reliability of the TLS, and hence its environmental efficiency include the more complete use of available biological and physical environmental variables, adjusting the time period that observational data are registered and modelled data are integrated, adjusting the interpretation of data including recognition of uncertainty in model outcomes, and use of more realistic assumptions concerning lice‐induced mortality thresholds.

Environmental, economic and energy evaluation of alternative fuels for a steam power plant: Focus on biodiesel-nanoparticles utilization

Power plants are among the largest producers of GHGs. The fuel used in power plants is the main factor in the production of the emissions. Therefore, choosing a fuel that is environmentally optimal while being energy-efficient and economical is a key way to reduce GHG emissions. The addition of nanoparticles to biodiesel fuel not only improves the combustion efficiency of the fuel but also reduces the undesirable products. In this research, three supplementary fuels - fuel oil, diesel, and biodiesel-nanoparticles - are compared in terms of environmental, economic, and energy efficiency in a steam power plant. The changes in the power plant's self-consumption were measured by calculating the power consumption of the pump that delivers the fuel to the boiler. The results showed that diesel reduced the plant's efficiency by 0.00022 compared to using natural gas, which was the least of the other alternatives. The environmental analysis revealed that diesel produced the least amount of CO2 eq, but biodiesel-nanoparticle had a better CO2 footprint due to the higher absorption of CO2 in the cultivation phase of the raw material for biodiesel. The economic analysis for the fuels was carried out over ten years and based on the lifetime of the equipment purchased. Consequently, the total cost over the ten years for diesel was $967332.5161, which was $124475.8381 less than that for biodiesel-nanoparticle and $240935.3341 less than that for fuel oil. Finally, an overall comparison was made between the fuels using the AHP method. As the environmental criterion was the most important decision criterion, biodiesel-nanoparticle fuel was chosen with a marginal difference compared to diesel.

A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development.

Approaches to sustainable development of production systems in the sphere of housing construction on the basis of application of “green” standards

Aim. To determine the problematics of the application of “green” standards in the field of housing construction and its impact on the management of sustainable development of production systems based on the application of “green” standards in the marketing sphere, as well as to propose options for solutions to problematic issues and directions for further research.Objectives. To analyze statistical information, as well as regulatory documentation in the field of “green” certification of residential real estate; to identify the problems of applied standards; to offer possible solutions to problematic issues in the aspect of application of “green” standards in the marketing sphere; to determine the necessary measures to implement the principles of sustainable development of production systems in general and adaptation of “green” standards in the assortment policy, marketing sphere of production systems in particular; to determine the directions for further research.Methods. When conducting the research, general scientific methods such as general analysis, expert assessments and generalization, document analysis, abstraction, comparison were used.Results. In the course of the study, the research revealed consolidated groups of problems, including “green” certification in the sphere of individual housing construction in conditions of non-systemic control of the process and results of activity in the marketing sphere of production systems (defined as a goal of further research); the problems of application of the approved and implemented in 2022 “green” state standard in the field of apartment building were revealed. During the analysis of regulatory documentation (GOST R 70346-2022. “Green” standards. Buildings of multifamily residential “green”. Evaluation methods and criteria for design, construction and operation) methodological problems of applying the criteria of group No. 6 “Materials and resource efficiency” are studied, possible solutions are proposed, directions for further research are outlined, the impact of these issues on the complex of management of sustainable development of production systems based on the application of “green” standards in the marketing sphere is shown. In addition, the classification of building evaluation stages in terms of construction stages is given.Conclusions. Approaches to the sustainable development of production systems in the sphere of housing construction, production and application of building materials have a special significance in public policy. An example is the “green” state standard, which is adopted in 2022, its popularization including through “Dom.RF”. However, the current certification mechanism, with all its advantages and merits, has a number of limitations in its application: lack of information from the developer, especially about completed objects, lack of comprehensive systems and registers of “green” solutions and materials, insufficient regulatory mechanisms in case of conflict between environmental friendliness and economic efficiency. These limitations are analyzed in the context of the block of criteria № 6 “Materials and resource efficiency”, and possible solutions are proposed, including the development of a comprehensive system and register of solutions and materials that meet the principles of sustainable development; accumulation of optional but necessary data on the certification of materials and solutions by the developer; proportional reduction of the tax base to compensate for the additional costs arising from the use of more expensive but more environmentally friendly materials and solutions; introduction of a new system and registry of “green” solutions and materials that meet the principles of sustainable development. The authors also identify directions for future research.

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

City-level green growth accounting: Evidence from China's thirteen urban agglomerations

Cities play a pivotal role in achieving 'Carbon Peak' and 'Carbon Neutrality' objectives through the implementation of strategies aimed at mitigating environmental risks. While the environmental impacts of industrial production activities have been widely examined, the nuances of their internal structures remain obscure. This study delves into the industrial sector across the 'thirteen urban agglomerations (TUAs)' in mainland China, covering the years 2006–2016, by developing a comprehensive (source-specific and variable-specific) decomposition framework for Malmquist productivity index. The framework is utilized to discern whether efficiency changes or technological advancements drive productivity growth, considering input/output variables such as capital and labor. Findings show that the average annual environmental productivity gain during the examined period was 2.6 %, suggesting a general enhancement in productivity within TUAs' industrial sectors. A detailed breakdown of productivity changes indicates that a combined contribution of 1.8 % to environmental productivity growth stemmed from energy use and pollutant variables, with emissions of industrial sulfur dioxide being the most significant at 0.9 %. Conversely, the 'catch-up effect,' or environmental efficiency change, was negative (−0.2 %), indicating the TUAs' inability to emulate the productivity levels of more advanced areas. Industrial energy use and capital inputs were the primary contributors to this negative trend, each accounting for a −0.2 % impact. The results underscore the importance of facilitating technology transfers from more developed to less advanced regions, especially regarding renewable energy and capital investment, to bolster environmental performance and productivity in the TUAs' industrial sectors.

Towards sustainable development goals: An analysis of environmental efficiency and the impacts of self-purification capacity across diverse income levels

The increasing urgency of global environmental degradation, particularly across diverse economic development stages, underscores a critical need for nuanced understanding and targeted strategies to achieve Sustainable Development Goals. Our study examines environmental efficiency trends over 27 years in 163 countries, utilizing greenhouse gases and particulate matter 2.5 as indicators. We address the challenge by developing and applying a two-stage method that combines a hyperbolic distance function with a stochastic meta frontier approach to assess environmental meta-efficiency. The average meta efficiency of these countries is 0.464, which remains at a relatively low level. Our model indicates that the high-income country group needs to reduce greenhouse gas and pollutant emissions by 25% and increase non-fossil energy usage by 33% to improve environmental efficiency. This suggests these countries must transition towards more sustainable energy sources and practices. Moreover, recognizing that existing income grouping inadequately characterizes each country, we use k-means cluster analysis for regrouping, more accurately reflecting individual differences. The regrouping results show that some high-income countries are classified into inactive groups, implying serious environmental problems. Our findings advocate for collaborative and tailored strategies to address these disparities. We conclude that income levels cannot solely drive environmental efficiency but must also consider geographical and climatic factors, which are pivotal in shaping a country’s environmental policies and efforts. This approach offers a clearer understanding of current inefficiencies and sets the stage for more informed policy-making that can better address the specific needs and capabilities of different countries.

Mechanochemical synthesis and catalysis of dinuclear Cu (II) complexes in C–S coupling

This study introduces a mechanochemical approach for synthesizing dinuclear Cu (II) complexes and applying them directly in C–S coupling reactions, offering a sustainable alternative to traditional solvent‐based syntheses. The mechanochemical method, recognized for its environmental advantages and efficiency, was employed to synthesize the Cu (II) complex [CuL(μ‐Cl)Cl2] (I) and the metal–organic salt 2L.2[CuCl4]2− (II), thereby minimizing solvent use and waste production. The synthesized complex I exhibited superior catalytic activity in C–S coupling, achieving a 96% yield within 20 min with methanol serving as a grinding medium. The mechanochemical process was further refined and confirmed for its environmental sustainability, with an E‐factor of 0.460 and an EcoScale score of 78. This synthetic method not only shortens reaction times and reduces solvent use but also streamlines the operational procedure, providing a more environmentally benign and economically viable route for C–S coupling. The research highlights the potential of mechanochemistry in simplifying synthetic processes and enhancing their sustainability, paving the way for future advancements in green synthetic strategies.

Eco-efficient ultra-high-performance concrete formulation utilizing electric arc furnace slag and recycled glass powder–advanced analytics and lifecycle perspectives

Ultra-high-performance concrete (UHPC) is a promising material for sustainable and resource-efficient construction, but its high Portland Cement content poses environmental challenges. This research proposes an optimized UHPC blend that significantly reduces the Portland Cement dosage by incorporating industrial waste materials [i.e., Recycled Glass Powder (RGP) and Electric Arc Furnace Slag (EAFS)]. The integration of these waste materials in UHPC formulation addresses critical waste management issues and aligns with sustainable construction practices. Utilizing a tri-variable Design of Experiments methodology in conjunction with the Andreasen & Andersen theory, the research aims to reduce Portland Cement in UHPC without compromising its compressive strength and rheological behavior. The optimized blend incorporates RGP and EAFS, achieving the threshold compressive strength established by ACI-239 (150 MPa) while maintaining a self-compacting performance in the fresh state. This mixture features low cement (621 kg/m³) and silica fume (100 kg/m³) content. Life cycle assessments (LCA) were conducted to examine the environmental efficiency of the optimized blend. A comparative LCA demonstrates that the optimized UHPC mixture developed in this study surpasses a control mixture with higher cement and silica fume contents while maintaining comparable mechanical and rheological properties. Notably, this improvement is highlighted by a nearly 20% reduction in CO2 equivalent emissions. The scientific significance of this study lies in its demonstration of a viable pathway to producing UHPC with reduced environmental impact, contributing to the advancement of sustainable building practices, and offering a practical solution to waste management issues.