Cost Consumption Research Articles

Visualization and Parameters Determination of Supersonic Flows in Convergent-Divergent Micro-Nozzles Using Schlieren Z-Type Technique and Fluid Mechanics

Small-scale and supersonic convergent-divergent type micro-nozzles with characteristic sizes of around a few centimeters and exit and throat radii of tenths of millimeters were the subjects of this study. Using the schlieren Z-type optical technique, the supersonic airflows established at the exit of seven nozzles were visualized. The dependence of the shock cell characteristics on the nozzle pressure ratio (NPR), defined as the ratio of stagnation pressure to atmospheric pressure, was analyzed. The dependence of the nozzle thrust and the specific impulse on the NPR ratio and the mass flow rate was also studied using a simple device based on concepts of fluid mechanics. The results obtained are in agreement with similar results obtained in recently published research on double-bell nozzles. The thrust of all nozzles depends linearly on the shock-cell spacing, which is one of the most relevant findings of this research. In other words, the output airflow structure determines the performance of the nozzles, such as the thrust or the specific impulse they produce. These small nozzles offer significant advantages over conventional nozzles in low energy consumption and lower manufacturing cost, making them suitable for scientific research in space micro-propulsion and cooling microelectronic systems, among other applications.

A Model for Vehicle Routing Problem under Returns and Emission Consideration in B2C E-Commerce Logistics

Purpose: With the widespread use of the Internet, electronic commerce allows customers to purchase products from the virtual stores of businesses instead of physical stores. This study addressed a mixed-integer linear programming model for a vehicle routing problem under returns and emission considerations in B2C e-commerce logistics. Methodology: This study proposes a mathematical model to solve a variant of the vehicle routing problem. The objective is to minimize the fuel consumption cost, penalty cost for unmet demand of returned items, and fixed cost for operating a vehicle. A clustering-based solution algorithm has been introduced to solve large-sized instances within reasonable solution times. Findings: The numerical analysis for the base case shows that the suggested model can assist decision-makers in coordinating forward distribution and reverse collection decisions within the context of sustainable e-commerce logistics. The result of the adjusted model for minimizing emission shows that a reduction of nearly 17% in total emission amount can be achieved, however, the adjusted model postpones all demand for the collection of returned items. Furthermore, the cluster-based solution approach causes a considerable decrease in solution time while providing promising solutions. Originality: This study represents a contribution to the existing literature on the subject by considering: i) emission to determine effects on the vehicle routing problem, ii) the postponement of the collection of returned items due to the limited delivery time, iii) proposing the clustering-based solution approach to tackle with larger-sized problems.

Leveraging central-surrounding receptive fields for single image dehazing

Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, Vision Transformers (ViT) have shown promising dehazing performance, leveraging their capacity for global perception through long-sequence dependencies in cost of high resource consumption. Therefore, our approach seeks to integrate the utilization of global information into the Convolutional Neural Network (CNN) framework in a more resource-efficient manner. In this paper, we introduce the Adaptive Center-Surround Receptive Field (ACSRF) network architecture inspired by the central-peripheral receptive field in biological vision for single-image haze removal. This leads to a unique receptive field mechanism that effectively combines both central and surrounding information. Our ACRSF addresses this by initially compressing global information and then merging it within the CNN, significantly boosting the capability to integrate local and global information, and effectively handling dominant color tones. Experimental results on four publicly available real-world image dehazing datasets show that our ACRSF outperforms current state-of-the-art methods in recovering global information, especially in dominant color tones. Importantly, this technology demonstrates its effectiveness in realistic scenarios, contributing significantly to improving traffic safety in adverse weather conditions. The code is available at https://github.com/JavanTang/ACSRF.

Assessment of reactor configurations and key factors for enhanced anammox-based nitrogen removal.

Wastewater treatment processes are continually evolving to meet stringent environmental standards while optimizing energy consumption and operational costs. With significant advantages over more traditional approaches, the anammox process has become a hopeful substitute for nitrogen removal. The objective of this work was to evaluate upflow anaerobic sludge blanket reactor (UASBR), moving bed biofilm reactor (MBBR), and sequential batch reactor (SBR) among diverse reactor configurations, in culturing anammox bacteria and achieving nitrogen removal efficiencies. Synthetic wastewater containing NH4+-N concentration and NO2--N concentration of 80±5mg/L was introduced to the reactors, and observations were made for up to 150 days. This study found that the MBBR demonstrated superior anammox activity, achieving a total nitrogen removal efficiency (TNRE) of 94±3%, SBR exhibited a TNRE of approximately 85±3%, while UASB displayed TNRE of 73±3%. The effect of varying carbon-to-nitrogen (C/N) ratios on nitrogen removal efficiencies was investigated, revealing a decrease in TNRE as the C/N ratio increased from 3 to 8. This study demonstrated the enhancing and inhibitory effects of C/N ratio, NO₂--N, and Fe concentrations. It revealed that Fe concentrations between 1 and 5mg/L increase specific anammox activity (SAA), while concentrations between 5 and 10mg/L negatively impact it. Additionally, NO₂--N concentrations above 150mg/L significantly reduce SAA. Furthermore, a 16S rRNA metagenomic analysis of MBBR sludge samples revealed the significant presence of Candidatus Brocadia bacteria, constituting 20.4% of the microbial community. This research highlights the potential of MBBR in fostering anammox reactions and achieving efficient nitrogen removal in wastewater treatment applications.

Combined ventilation mode in extra-long road tunnels: Energy efficient channel utilization with the inclined shafts

The ventilation mode plays a dominate role in addressing the feasibility of an extra-long tunnel. In this paper, a combined ventilation mode, i.e. energy efficient channel utilization with the inclined shafts, is proposed to ensure the air quality and fire safety in the extra-long road tunnel. Combined with the equations of ideal air, mass and energy conservation, and boundary conditions, the velocity of internal airflow and the energy consumption of tunnel can be analytically solved. As regards the case study in the Tongzi Tunnel in China, the scheme of combined ventilation mode of single channel and inclined shafts was proposed. The effects of natural wind pressure, traffic wind pressure and mechanical ventilation pressure on tunnel ventilation energy consumption were considered. In addition, the energy efficient channel is a strong substitution for shafts to enhance the efficiency of the ventilation. Based on the results, the position of the vertical shaft and single channel in combined ventilation was optimized. Compared with original ventilation scheme, the new method can save up to 40% ventilation energy consumption in daily operation, and saving 1.02 million CNY in electricity costs per year and improving long-term economic efficiency. In the fire emergency, the combined ventilation mode can more effectively control smoke for larger scale tunnel fires. In particular, when the power of the fire source exceeds 30 MW, the required critical wind speed increase rate is reduced to 0.01 m/s per MW. The analysis of ventilation mode undertaken here, has extended our knowledge of reducing energy consumption and maintenance costs in the extra-long tunnel operation.

Environmental Assessment and Eco-Efficiency Analysis of the Dividing Wall Distillation Column for Separating a Benzene–Toluene–Xylene Mixture

The benzene–toluene–xylene (BTX) system represents an energy-intensive petrochemical process with various industrial applications. Global climate changes have forced modern industry to act toward environmental safety, which requires technological changes. Thus, the divided wall column (DWC) represents a significant advancement in multicomponent mixture separation. To assess the impact of the conventional BTX process and its intensification proposal based on DWC technology, it is necessary to integrate an eco-efficiency approach that jointly analyzes the economic and environmental variables influencing the system, such as water consumption, CO2 emissions, and utility costs. An auxiliary utility plant was also considered for more realistic results in terms of energy and water consumption, which was identified as a lack in many research studies that performed an overall sustainability analysis. The results showed that the DWC scheme is 37.5% more eco-efficient than the conventional counterpart, mainly due to a 15.6% and 30.3% savings on energy and water consumption, respectively, which provided a 15.5% and 16.7% reduction on CO2 emissions and utility costs, respectively. In addition, all other environmental and safety indicators based on the waste algorithm reduction (WAR) were reduced by approximately 16%. Thus, the DWC proved to be a convenient technology with economic attractiveness and environmental friendliness.

An integrated smart water management system for efficient water conservation

Water is a fundamental resource that sustains life, supports ecosystems, and plays a crucial role in various natural processes on earth. Water wastage is a major problem in the world, with a variety of causes including leaks in infrastructure and inefficient usage methods. A typical cause of water wastage is overflow from reservoirs or tanks as a result of poor maintenance or monitoring. This paper proposes a novel water resource management using internet of things (WARM-IoT) system to monitor and regulate the water level remotely by integrating IoT technology with demand side management (DSM) strategies, real-time monitoring of water levels has been achieved. The approach utilizes an ultrasonic sensor and Raspberry Pi for data acquisition and processing, fuzzy logic for decision-making, and an Android app for remote monitoring and control. The WARM-IoT assesses the system's performance, showcasing its efficacy in managing water levels and lowering electricity expenses. By analyzing consumption costs under different activation timings, significant potential for cost savings is observed, with a notable reduction of up to 6% in electricity expenses. Overall, the proposed WARM-IoT offers a comprehensive solution to water wastage and inefficient electricity usage in water management systems.

Cooperative scheduling of airport ground electric service vehicles considering workload balance: A column generation approach

Under the trend of electrification in civil aviation, airports are promoting electric ground vehicles to reduce carbon emissions. This paper conducts on electric airport ground service vehicles in scheduling optimization problems. Moreover, driver psychology is also a growing concern as it can affect the efficiency and safety of ground services. Workload balance is one of the major factors affecting driver psychology. In the context of facing challenges for ground vehicle scheduling, fully using electric vehicles and guaranteeing the workload balance are major concerns in our study. This paper investigates the electric shuttle buses and towing tractors cooperative scheduling considering workload balance optimization problem (e-STCSWB). We introduce a mixed integer programming formulation for e-STCSWB that considers cooperative relationships, workload balance, energy consumption and time windows to minimize total energy consumption costs. The e-STCSWB is obtained small scale exact solutions using the CPLEX solver. Furthermore, a column generation approach is developed, which is enhanced through a problem-specific strategy. Extensive computational results using real-world instances from one of China’s largest airports show that it can obtain optimal or near-optimal solutions. The problem-specific strategy significantly improve the performance in terms of solution quality and solution time. Finally, we perform sensitivity analyses to demonstrate the impact under different numbers of vehicles and workload balance requirements parameter settings.

Green Computing Optimization for Multi-Region Streaming Platforms

Global streaming services are always under pressure to control their resource usage effectively while still offering millions of users worldwide flawless experiences. As user demands for continuous, high-quality content increase, it is becoming increasingly important to strike the right balance between low latency, high availability, and resource efficiency. However, the infrastructure required to meet these demands often results in significant energy consumption and operational costs, presenting a major challenge for sustainability. The application of green computing principles to multi-region cloud infrastructure optimization is examined in this article, with an emphasis on tactics that minimize energy consumption and operational costs without compromising the performance that users rely on. Platforms can preserve their competitive advantage while making significant progress toward environmental responsibility by implementing more intelligent, energy-efficient procedures.

Influence of beam-column connection tension on the overall stability and steel consumption for a low-rise reinforced concrete building

This study investigates the role of embedment in beam-column connections on the overall stability and steel consumption in reinforced concrete structures. Proper embedment is crucial for ensuring structural stiffness and stability, significantly affecting the building's behavior under loading. While higher embedment degrees enhance stability, they may also increase steel consumption. Thus, optimizing embedment can strike a balance between safety and material efficiency. This research evaluates the stability and steel consumption in a reinforced concrete multifamily building by varying the embedment degree of beam-column connections. The study also examines the impacts of embedment on horizontal displacements and beam bending moments. Structural behavior was analyzed using computational simulations performed with TQS® software. The results reveal that fully encased connections provide superior stability but at the cost of higher steel consumption. Conversely, partially encased connections offer a more balanced solution, maintaining satisfactory stability while reducing material usage. These findings highlight the potential for optimizing embedment degrees to achieve efficient and safe designs for reinforced concrete structures.

The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis

Predicting road freight prices is a challenging task influenced by multiple factors. Understanding which variables have the greatest impact is essential for building more accurate models, and consequently for enhancing the competitiveness of Brazilian soybeans in the global market. This study aims to evaluate the influence of different exogenous variables on soybean freight prices and to analyze how this influence varies across different distance ranges. To achieve this, a combination of machine learning techniques was applied to a comprehensive dataset containing information on freight costs, regional characteristics, production, fuel prices, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, directly reflecting operational expenses such as fuel consumption and labor costs. Additionally, macroeconomic factors such as the exchange rate and export volume play a crucial role, highlighting the global context of Brazil’s soybean exports. Stratified analysis by distance ranges reveals distinct patterns; short-distance freight is predominantly related to domestic markets, while medium- and long-distance freight are strongly linked to export logistics.

An Optical Differential Method for Underwater Wireless Communication in Turbid Environments

Underwater optical communication has emerged as an essential tool for exploring oceanography and marine resources for underwater vehicles or robots in recent years. Current techniques mostly rely on the paradigm of intensity modulation and direct detection, resorting to more powerful light sources on the transmitting side and more sensitive detectors on the receiving side, thus causing excess energy consumption and system costs. Here, a novel approach, namely, the optical differential communications method (ODCM), is proposed to extend the distance of underwater wireless optical communications in turbid water. The underlying physical reason is explained in theory and demonstrated in experiments. It is found that the stable propagation distance of ODCM could be further extended without relying on intensive light sources, in contrast to conventional methods, showing potential for longer communication ranges. Tests of underwater optical communications are conducted, and the results show that ODCM can significantly reduce the bit error rate (BER) at the same propagation distance or extend the propagation distance for the same BER level of optical signals. As such, this study provides an avenue for long-distance and stable underwater wireless optical communications in turbid environments.

Recent Advances in Detection and Control Strategies for Foodborne Bacteria in Raw and Ready‐to‐Eat Fruits and Vegetables

ABSTRACTThe prevalence of foodborne outbreaks due to the consumption of uncooked and ready‐to‐eat fruits and vegetables has seen a noticeable increase, particularly in environments lacking sanitation. This article extensively explores recent advancements in the detection of foodborne pathogens in uncooked and ready‐to‐eat fruits and vegetables, alongside potential prevention strategies. Predominantly, pathogens like Listeria monocytogenes, Escherichia coli, and Salmonella enterica are the main culprits in outbreaks linked to these food items globally. Notably, contamination is more prevalent in fresh leafy greens than in fruit products. Various detection methods such as culturing, microscopy, immunological assays, polymerase chain reaction (PCR), biosensors, and hyperspectral imaging have proven effective in identifying pathogens in these foods. Nonetheless, these methods come with challenges, including time consumption, accuracy concerns, and high costs. Research is ongoing to refine these detection techniques, with efforts including combining methodologies like PCR–enzyme‐linked immunosorbent assay and integrating culturing with PCR. Additionally, several interventions, including cold plasma treatment, ultraviolet irradiation, and the application of edible coatings, have shown promise in mitigating contamination risks, thereby enhancing the safety of these fresh produce items.

Green Extraction of Carotenoids from Pumpkin By-Products Using Natural Hydrophobic Deep Eutectic Solvents: Preliminary Insights

Natural hydrophobic deep eutectic solvents (NaHDESs), composed of natural components like menthol, fatty acids, and organic acids, are sustainable alternatives to conventional solvents for extracting carotenoids from agro-industrial by-products. This study assessed the performance of nine NaHDESs for extracting β-carotene from pumpkin peels, identifying DL-menthol/lactic acid (1:2) as the most effective solvent, achieving a yield of 0.823 ± 0.019 mg/mL of β-carotene, corresponding to 93.95% of the yield obtained using acetone. Optimization through Box–Behnken design (BBD) and response surface methodology (RSM) established ideal extraction conditions: a molar ratio of HBA:HBD at 1:4, a solvent-to-sample ratio of 26:1, and an extraction time of 30 min. These conditions maximized β-carotene recovery while minimizing energy consumption and process costs. Using NaHDESs facilitates the valorization of food waste, achieving extraction efficiencies of up to 25.05% of the theoretical carotenoid content in pumpkin peels. Their high performance and environmentally friendly profile underscore the potential of NaHDESs as sustainable alternatives to conventional solvents.

Utilizing Waste Glass Aggregates in Concrete: Enhancing Performance and Reducing Carbon Emissions through Life Cycle Assessment

To effectively address the issue of waste glass disposal, this paper proposed a novel approach of using waste glass aggregates as mineral admixtures in concrete. The study investigated the characteristics of waste glass aggregates and their effects on the compressive strength, flexural strength, and fluidity of concrete. Additionally, based on the life cycle assessment (LCA) method, the carbon emission reduction benefits of using waste glass aggregates were analyzed and calculated. The results indicate that with the increase in the content of glass fine aggregates, the fluidity of concrete increases, with larger particle sizes contributing to higher fluidity. Compared to ordinary concrete, the compressive and flexural strengths of waste glass aggregate concrete do not show a significant reduction. Moreover, the recycling and utilization of waste glass can reduce energy consumption and production costs, save resources, and reduce environmental pollution, which is significant for achieving the "dual carbon" goals in the construction industry.

Multi-UAV cooperative task assignment based on multi-strategy improved DBO

Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.

The Generation Load Aggregator Participates in the Electricity Purchase and Sale Strategy of the Electric Energy–Peak Shaving Market

To facilitate the participation of small- and medium-sized customer-side resources into the electricity market, with the aim of optimizing the allocation of electricity resources, this paper proposes the participation of small- and medium-sized adjustable resources in the electricity market in the form of generation load aggregators. Considering the coupling role of the electric energy market and the peaking auxiliary service market, a joint model of generation load aggregators that participate in the electric energy–peak shaving market is constructed. Comparing the model solution with those of the control group, the peak-to-valley difference of 10.5 MW is much lower than that of the control group, which is 16.54 MW. Compared with the control group, the profit was increased by USD 2.6 thousand, or 6.06%. It can be seen that the model proposed in this paper can reduce the peak and valley pressure as well as the control pressure of the power system from the load side. By participating in the peak shaving market, the transferable loads within the generation load aggregators can give full play to its adjustable characteristics, thereby reducing the cost of electricity consumption and increasing its profit, and providing a certain theoretical basis for the electricity market management to design the rules for adjustable users to enter the electricity market.

Time-series machine learning for predictive optimisation of a highly efficient evaporative cooling system

As data centres become integral to modern infrastructure, their energy consumption, particularly in cooling systems, presents a critical challenge for sustainability. This paper addresses this issue by applying time-series machine learning models to forecast the performance of a highly efficient 100 kW evaporative cooling system applied in a real-world data centre. Using a dataset spanning 4 months, we developed and optimised two predictive models based on XGBoost and Random Forest, to estimate cooling capacity and Coefficient of Performance (COP). Initial results showed suboptimal performance, with the XGBoost model achieving a Mean Absolute Error (MAE) of 1.34 for cooling capacity and 6.50 for COP, alongside a negative R-squared, indicating poor fit. However, after hyperparameter tuning, the Random Forest model significantly improved the predictions, achieving an MAE of 0.39 and an R-squared of 0.85 for cooling capacity, and an MAE of 2.21 and an R-squared of 0.54 for COP. These findings underscore the potential of these models to optimise cooling efficiency, offering valuable insights for reducing energy consumption and operational costs in data centre operations. This research paves the way for more sustainable data centre designs and operations across diverse climatic conditions. Practical application The predictive models developed in this study enable building environment professionals to optimise data centre cooling systems. By accurately forecasting cooling capacity and Coefficient of Performance (COP) under varying environmental conditions, these models allow for proactive adjustments to cooling strategies, ensuring efficient operation and minimising energy waste. This research provides a practical tool for enhancing the sustainability of data centres, directly supporting industry efforts to meet stringent energy efficiency targets and reduce the carbon footprint of critical infrastructure.

Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks

Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.98%. Furthermore, the study employs the KNN-based cost-efficient method to predict nodes’ orientation in dynamic water conditions, thereby facilitating the mapping of the shortest distance between sensor nodes during the underwater context acquisition process. The effectiveness of the proposed KNN-based cost-efficient method is evaluated through real-time experiments conducted in a water tank setup and simulations using the Ns-3.37 version. Results demonstrate notable improvements in localization accuracy by optimizing the localization error rate from 4.59m to m, Reducing localization energy consumption rate 0.0045J in addition for the first time we have also computed the localization Time cost rate which is 0.06762s. we assumed that in real-time and in NS-3 simulations on the Aqua-sim model indicate communication speed at 1500m/s. This research presents an innovative and practical approach to resolving challenges associated with underwater context acquisition through sensor nodes, it offers a comprehensive understanding and emphasizes the real-time implementation of the KNN-based cost-efficient approach.

Optimization of airflow distribution in mine ventilation networks considering ventilation energy consumption and number of regulators

In view of the problem of high energy consumption and high control costs caused by uneven airflow distribution and unreasonable control in complex mine ventilation networks, this study takes the minimum ventilation energy consumption and number of regulators as optimization objectives to establish a multi-objective optimization model for airflow distribution in mine ventilation networks. Based on the roadway adjustable attributes and the minimum spanning tree principle, the location of regulators was reasonably determined. Moreover, this article proposes an improved invasive weed optimization (IIWO) algorithm to solve the optimization model with high coupling and nonlinearity. Compared with other algorithms, IIWO showed excellent optimization performance. IIWO was applied to optimize the airflow distribution in the mine ventilation network. The results show that the algorithm can effectively reduce the energy consumption and number of regulators of the ventilation network, and the energy saving rate of the fan is 31.78%.