Environmental Efficiency Research Articles

Активизация пластовой микрофлоры для повышения нефтеотдачи

Constant optimization of technological processes of oil production is one of the integral parts of rational subsurface use. The study and implementation of new developments and technologies in the oil production process is primarily due to the economic effect. Methods of increasing oil recovery are of particular interest in the practical implementation of innovative technologies in the oil and gas industry. To date, a huge number of technologies have been developed and tested in field conditions aimed at preserving and increasing the volume of produced products, the classification of which is very diverse. The primary criteria for choosing a method or combination of methods of influencing a productive reservoir in order to increase oil recovery are the geological conditions of the productive horizon, as well as the composition and physico-chemical properties of reservoir fluids. Currently, of all the many methods of increasing oil recovery, the microbiological method is the least studied, as a result of which it is extremely rarely used in enterprises. The relevance of research on the effects of microorganisms on the reservoir rock is beyond doubt. The possibility of widespread application of oil recovery enhancement technology based on bacterial life processes is interesting both from the point of view of environmental safety and economic efficiency. One of the microbiological methods of increasing oil recovery is the activation of the existing reservoir microflora by feeding a nutrient medium into the reservoir. In the course of their vital activity, some types of bacteria are capable of destroying clay rocks, which entails changes in the filtration and capacitance properties of the reservoir rock and, as a result, the volume of products produced increases significantly. The advantage of this method is the absence of the need for forced “contamination” of the reservoir formation, since the necessary bacteria are already in it.

Digitalization of Mining Enterprise: A Key Factor in Ensuring Economic Security

The level of competitiveness of mining companies is primarily determined by productivity and operational excellence. Digitalization is becoming a key factor that enables companies in this industry to remain competitive in the future. In the mining industry, the transition to new and better ways of working has been slow due to the scale and complexity of production processes, as well as the significant costs of modernization. As a result, some enterprises are not actively implementing digital technologies. However, leading global and Kyrgyz mining companies are actively investing in the development of modern technologies in the field of energy and automation in order to increase production volumes, improve environmental efficiency, reduce the use of manual labor, costs and energy consumption. It is obvious that digital technologies open new opportunities for a significant increase in labor productivity and profit growth. The article discusses the main directions of digital transformation of mining enterprises, reveals the key advantages and potential threats. The author analyses both foreign and Kyrgyz examples of effective digitalization in this industry.

Speed planning and control in complex road conditions based on vehicle driving capability

In order to solve the problem of vehicle speed planning that meets the constraints of safety and efficiency in complex road environments (large curvature and low adhesion), a differential equation planning method based on vehicle dynamics analysis is proposed. First, the structural parameter expression of the limit speed that meets the lateral tire force constraint during steady-state steering is derived. Secondly, the tire force execution space of the front and rear wheels is combined into an FF diagram, and an implicit differential equation that considers load transfer and driving mode factors is obtained. The differential equation is solved to obtain the limit speed along the path, and a limit speed calculation method for discrete path information is given. Finally, a lateral and longitudinal collaborative model predictive controller is designed, and a CarSim and Simulink joint simulation platform is built. Trajectory tracking simulation experiments are carried out on both continuous and discrete information paths with the planned limit speed. The results show that the limit speed planning method proposed in this paper can complete the trajectory tracking task in complex road environments as quickly as possible while controlling the tire force within the range of the stable friction circle.

The Green Extraction of Blueberry By-Products: An Evaluation of the Bioactive Potential of the Anthocyanin/Polyphenol Fraction.

In the context of a circular economy, this study explores the valorization of blueberry pomace (BP) as a source of bioactive compounds using sustainable extraction methods. Microwave-assisted extraction (MAE) and microwave-assisted subcritical water extraction (MASWE) were employed to obtain two distinct fractions: MAE 1° and MASWE 2°. The first extract, MAE 1°, obtained at 80 °C, had a high total anthocyanin content (21.96 mgCya-3-glu/gextract), making it suitable as a natural pigment. Additionally, MAE 1° exhibited significant enzyme inhibition, particularly against α-amylase and β-glucosidase, suggesting potential anti-diabetic and anti-viral applications. The second extract, MASWE 2°, obtained at 150 °C, contained a higher total phenolic content (211.73 mgGAE/gextract) and demonstrated stronger antioxidant activity. MASWE 2° showed greater inhibition of acetylcholinesterase and tyrosinase, indicating its potential for use in Alzheimer's treatment, skincare, or as a food preservative. MASWE 2° exhibited cytotoxicity against HeLa cells and effectively mitigated H2O2-induced oxidative stress in HaCat cells, with MAE 1° showing similar but less pronounced effects. A tested formulation combining MAE 1° and MASWE 2° extracts in a 3:2 ratio effectively enhanced anthocyanin stability, demonstrating its potential as a heat-stable pigment. The extract characteristics were compared with a conventional method (MeOH-HCl in reflux condition), and the protocol's sustainability was assessed using several green metric tools, which provided insights into its environmental impact and efficiency.

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.

Control of Liquid Hydrocarbon Combustion Parameters in Burners with Superheated Steam Supply

A numerical simulation of reacting mixture flow in a full-scale combustion chamber of a prototype burner with a fuel-sprayed jet of superheated steam and a controlled excess air ratio was performed based on a verified model. The influence of steam jets on the combustion parameters of the created prototype device was analyzed based on the results, and a comparison with data from various atmospheric burners, including evaporative and spray types, direct-flow and vortex types, and those with natural and forced (regulated) air supply, was made. Various schemes for supplying steam to burner devices were discussed. It was shown that the relative steam consumption is a parameter for controlling the emission of toxic combustion products, such as NOx and CO, for all designs. A high burner performance is achieved when superheated steam is supplied at more than 250 °C with a relative steam flow rate of >0.6. The design features of the burner systems and operational parameters that ensure high thermal and environmental efficiency when burning various types of fuel and waste are identified.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

Fault diagnosis is critical in maintaining the operational stability and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems, which are crucial for ensuring indoor environmental quality and energy efficiency in buildings. However, traditional fault diagnosis methodologies face substantial challenges in accurately capturing the dynamic interactions within these systems and effectively utilizing unlabeled data. To overcome these limitations, an innovative fault diagnosis approach utilizing the Adaptive Fusion Graph Convolution Network (AFGCN) is proposed in this paper. This method significantly enhances the model's learning and inference abilities, particularly in scenarios with limited labeled data, by adaptively integrating the associative graph features of both unlabeled and labeled data. Furthermore, to augment the transparency and trustworthiness of the diagnostic outcomes, this paper introduces an interpretability analysis module. This module is designed to quantify the contribution of each sensor node in the fault diagnosis process. Experimental evaluations using the ASHRAE RP-1043, actual building operating chillers datasets, and LBNL FDD Data Sets_SDAHU indicate that this method offers substantial performance improvements in diagnosing faults in HVAC systems.

Enhancing RODNet detection in complex road environments based on ESM and ISM methods

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.

Sustainable mega-seaports with integrated multi-energy systems: Life-cycle environmental and economic evaluation

Ports play a critical role in modern society by acting as crucial links between water and land transportation, and integrating transportation with energy systems. This integration results in a high demand for various types of energy uses, with polluting emissions produced by the diverse energy sources. Integrated renewable energy systems represent promising solutions to achieving high levels of energy supply while lowering carbon footprints. In this research, a framework is proposed for a port multi-energy system that encompasses solar energy, wind energy, a hydrogen system and a number of energy storage systems. The proposed framework is tailored for implementation at Ningbo Zhoushan Port, the largest port globally. Different system design schemes are compared based on the number and rated power of wind turbines. Then, a comprehensive life-cycle economic and environmental assessment of the system is conducted through simulation. The economic and environmental metrics such as LCOE, REF and CO2 emissions are considered. The outcomes demonstrate that the design scheme incorporating two wind turbines with high rated power outperforms others in both environmental and economic metrics. Over the life-cycle, the renewable energy fraction exceeds 72%, and the levelized cost of energy plummets to 0.46 yuan/kWh. The implementation of the proposed port integrated multi-energy system yields substantial environmental and economic benefits. Specifically, it allows for a reduction of 66.68% in CO2 emissions and a cost reduction of 70.94%. These outcomes highlight the potential that the proposed system holds for enhancing environmental sustainability and economic efficiency within the port context.

A comprehensive propulsion system analysis: an integrated approach utilising engine simulation and free-running model experiments

Regulations concerning the environmental impact and energy efficiency of ships are becoming increasingly stringent. To meet these demanding criteria, new technologies are actively being developed to achieve emission reductions while maintaining efficient fuel energy conversion into propulsion power. However, the propulsion system behaviour in a dynamic environment of the actual sea is highly nonlinear and complex, and a profound understanding of the mutual interactions is still lacking. This paper proposes an integrated approach for comprehensive propulsion system analysis. The key feature of this technique is the utilisation of an intelligent propeller drive controlled by a marine diesel engine simulator (MDES) to drive the free-running model of a ship, providing real-like behaviour of the engine model in real-time in response to actual measured values. The core of the MDES consists of a continuous cycle-mean value dynamic engine model supplemented with a detailed combustion cycle evaluation. The developed fast calculation algorithm of crank-angle resolved thermodynamic equations of the gas state in the engine cylinder enables the complete engine model to be integrated into the real-time environment of a physical model ship. Thus, the propulsion system's performance can be analysed under conditions that closely mimic those of the actual sea environment.

The role of digital twins in driving sustainability

The role of digital twins in driving sustainability As the pharmaceutical sector endeavours to become more sustainable, we hear how digital twins – virtual replicas of systems or products that can help predict performance – are supporting the industry in its efforts. The manufacturing sector is facing growing pressure from consumers, regulators, and investors to reduce its environmental impact. The shift towards more sustainable pathways is not only driven by regulatory compliance but also by future-proofing businesses in a world where resources are finite. This shift is clear in the pharmaceutical industry, which is striving to enhance the economic and environmental efficiency of its manufacturing processes while maintaining the highest quality standards.

Genotype-by-environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications.

Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype-by-environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene-nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.

Enhanced machining of Al 10%SiCmicro 1%SiCnano hybrid composite using rotary tool rotary workpiece EDM with bio dielectrics and treated tools

A sustainable approach was proposed to address environmental pollution, carbon footprint and economic efficiency challenges in Electrical Discharge Machining (EDM). This approach involved the use of Bio-dielectric such as biodiesel and Bio fuel (distilled water with 10% ethanol). The EDM process performance was further optimized by experimenting with both electrodes’ rotation (i.e., in same direction, opposite direction, no rotation) and the use of treated tools (no treatment, heat treatment, cryogenic treatment). Biodiesel as a bio-dielectric showed promise by delivering the highest Material Removal Rate (MRR) and the lowest Tool Wear Rate (TWR). Bio-fuel (distilled water with 10% ethanol) resulted in the lowest Surface Roughness (SR) and cleaner machined surface with least carbon deposition. Additionally, electrode rotation improved flushing and enhanced performance parameters, with opposite direction rotation yielding the highest MRR and the lowest SR. However, no rotation of electrodes resulted in the lowest TWR. The use of treated tools, specifically heat-treated and cryogenically treated tools, also improved performance and reduced energy consumption, with cryogenic treatment providing the highest MRR, heat treatment giving least SR, and no treatment providing least TWR. Certain interactions between factors significantly impacted performance parameters. Grey relational analysis revealed that using distilled water with 10% ethanol as a dielectric, employing cryogenically treated copper tools, and having no rotation of both electrodes yielded the best performance parameters.

Enhanced synergistic catalysis of bisphenol A in river water using an anti-aging photocatalytic membrane

Bisphenol A (BPA), as an endocrine disruptor, poses a potential threat to ecosystems and human health in aquatic environments. Membrane catalytic systems can accelerate the degradation of BPA and facilitate its conversion into harmless compounds. Nevertheless, the complex nature of the water environments and the limited stability of catalysts often result in challenges such as catalyst aging and deactivation. Herein, an anti-aging multifunctional AgFeO2 catalytic material with electron transfer membrane support was prepared for synergistic catalysis of low-energy LED light (12 W) excitation and peroxydisulfate (PDS) activation. The anti-aging photocatalytic membrane completely degraded 10 ppm of BPA within 30 min, and did not show significant aging after the long-term synergistic catalytic process. In addition, actual river water was employed to assess the aging process and catalytic efficiency in a practical environment. A 60.79 cm2 photocatalytic membrane completely purified 10 L of BPA polluted river water, while the total organic carbon content decreased by 50 %. This was mainly due to the synergistic catalytic effect of the membrane, which boosted photoelectron transfer through electron transfer shortcuts, thereby enhancing persulfate activation. Overall, the multifunctional membrane provides an effective strategy for achieving a long-lasting catalytic effect and controlling photocatalyst aging in practice.

Tradeoffs and synergies between social equity and environmental benefits in conservation

Conservation programs worldwide pursue social equity alongside environmental benefits and economic efficiency. When the spatial patterning of human diversity differs from the patterning of biological diversity, conservation planners face complex tradeoffs between social and biological objectives. Here, we quantify how these tradeoffs depend on the correlation between the spatial distributions of social and biological diversity. We used empirical patterns in the commonness and rarity of species to generate simulated landscapes with pre-defined correlations between biological diversity and human social diversity. Our analysis shows how tradeoffs between social equity and environmental benefits are unavoidable when human and biological diversity are negatively correlated. However, when human and biological diversity are strongly positively correlated, then biological and social priorities are congruent. In these settings, well-designed conservation programs may engender positive feedbacks between social equity and ecosystem services, enhancing both. Our analysis focused on distributional equity, but similar dynamics are likely to occur with procedural, recognitional and contextual equity. Given growing evidence that socially equitable conservation programs are more likely to be successful, our findings underscore the importance of carefully assessing the potential for conflicts and synergies between the social and biological goals of conservation programs.

Gaining in Giving: Unveiling Continuous Engagement Intentions with the Chinese Pro-Environmental Gamification Ant Forest

Gamification is increasingly recognized as a promising intervention to promote individual environmental contributions. However, the understanding of the motivations for users’ continuous engagement intentions with gamified environment protection remains insufficient. This study explores users’ continuous engagement intentions with Chinese pro-environmental gamification Ant Forest, using a research model that integrates the norm activation model, warm glow theory, and technology acceptance model. By analyzing quantitative survey data collected from 525 players of Ant Forest, the model is empirically examined through structural equation modelling. The research results show that Ant Forest users are motivated by a combination of altruistic, egoistic, and pragmatic factors. The identified positive impact factors include personal norms, ascription of responsibility, awareness of consequences, perceived warm glow, perceived ease of use, and perceived usefulness in improving users’ environmental contribution efficiency. Surprisingly, users find Ant Forest enjoyable as a game irrespective of its environmental benefits, but they are not primarily driven by the perceived pure fun from game playing. The findings of the study not only offer a comprehensive explanation for the antecedents of continuous engagement intentions with pro-environmental gamification but also provide theoretical and practical implications for gamification research and design in environment protection contexts.

Traffic road noise and transportation energy efficiency

Future noise emission limits for passenger car are going to lower levels by 2024 -Social cost of noise for France in 2021, shows clearly that the dominant source of noise pollution is road traffic (81 Bn€ for a total of 146 Bn€) This R51 regulation is meant to lower the noise pollution from road traffic, but when looking closer to the sources and their contributions, in particular the tire/road noise interaction, the environmental efficiency of this regulation is questionable. Tire/Road interaction involves tires characteristics, that are constrained by an array of specification and it has been proven that there is a physical limit to what could be expected from the tire as far as tire/road interaction noise is concerned.Road surface however, shows very wide acoustic characteristics spread : over 15dBA from the quietest to the loudest asphalt, to be compared to 2dBA steps in the regulation and an environmental impact that will depend on the rolling stock renewal rate. This paper will present a new patented method from Renault enabling to assess road acoustic data mapping from a simple drive of a Megane E-Tech, quickly and efficiently, issuing the first ever noise pollution control maps.

Environmental efficiency of rainfed and irrigated wheat crops in Spain. A stochastic DEA metafrontier approach

Environmental efficiency of rainfed and irrigated wheat crops in Spain. A stochastic DEA metafrontier approach

AI-induced job impact: Complementary or substitution? Empirical insights and sustainable technology considerations

This study utilizes 3,682 full-time workers to examine perceptions of AI-induced job displacement risk and evaluate AI's potential complementary effects on labor. By distinguishing between these variables, the research identifies which worker characteristics and industries are most affected by AI. Employing an extended ordered probit model, the results show that those with higher perceived risks are mainly female, older, and more educated. Workers with frequent internet usage and remote work are more concerned about AI-induced job risks. Sector-wise, entry-level employees perceive the least risk, whereas manufacturing professionals and senior service sector employees are more apprehensive. Contrary to the mainstream assumption that AI causes unemployment, this study identifies a potential complementary effect on job tasks, suggesting that AI can enhance and support human labor rather than replace it. This inference is based on observed worker perceptions and the increased use of AI tools in tasks that complement human skills. Moreover, AI's integration contributes to sustainable practices and entrepreneurial opportunities, enhancing business innovation and environmental efficiency. Workers increasingly view AI as a value-added element in their jobs rather than a threat, particularly in sectors focusing on sustainable development and green technologies.