Cost Consumption Research Articles

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.

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%.

Energy efficient green building design utlilising renewable energy and low-carbon development technologies

The core issue these days is to reduce carbon dioxide (CO2) emission and prevent the hole in the ozone layer. Numerous emerging environmental crises successfully tackled by coordinated global efforts in the past few years. Despite this, the present climate emergency is a much more serious threat than anything we have ever encountered and requires much more action consequently. Today, more attention is paid on green buildings in the society, which will make the use of sustainable buildings for the entire life cycle a necessity for future generations. Hence, this paper proposes a novel design model of an energy-efficient residential green building with low carbon emissions to maintain the health and enhances the productivity and living standards of inhabitants. Green building technology is utilized to enhance energy efficiency and lower carbon emissions. This design considers green, recyclable, and eco-friendly building materials, which are beneficial for human health and comply with relevant Indian standards and building codes. This building design proposes renewable energy sources (RES) integrated with the power grid, although RES powers most of the load of the green building. The proposed green building design shows effective results, i.e., building energy consumption has reduced by 50.54%, total energy consumption cost has reduced by 57.41%, and CO2 emission per month has reduced by 50.54%. In addition, stormwater-harvesting system is proposed to collect 54322.23 litres of rainwater annually, which helps in water conservation and contributes to improving the groundwater level. The proposed solid waste management plan has contributed to the achievement of regional and national Sustainable Development Goals (SDGs). Finally, there are some suggestions to promote the use of green buildings for sustainable development.

Treatment and prediction of wastewater from waste transfer station in the eastern rural area of China by a combined system of anaerobic-oxic-anoxic-oxic, coagulation and adsorption.

In this study, a combined system of anaerobic-oxic-anoxic-oxic, coagulation and adsorption (AOAOCA) was used to treat the real waste transfer station (WTS) wastewater. The effects of hydraulic retention time (HRT), sludge reflux ratio (SRR), mixed liquid reflux ratio (MLRR), coagulant and zeolite on the contaminants removal efficiency were investigated. When the AOAOCA system was operated at the optimal conditions (HRT of 8d, SRR of 70%, MLRR of 200%, PAFC as coagulant with dosage of 750ppm and 1-3mm zeolite with filling rate of 60%), the effluent COD, NH3-N and TP could reach 82.5mg/L, 3.7mg/L and 1.8mg/L with the highest removal rates of 98.7%, 99.4% and 98.8%, respectively. The effluent of AOAOCA system could meet the "Pollution Control Standard for Domestic Garbage Landfill of China" (GB16889-2024). The greatest contribution to the COD, NH3-N and TP removal rates were from the anaerobic-oxic-anoxic-oxic process (91.2%-97.4%), while the contribution of coagulation and zeolite were 1.8%-6% and 0.8%-2.8%, respectively. The operating cost for WTS wastewater treatment by the proposed AOAOCA system was 17.72 RMB/t based on the costs for electricity, reagent and tap water consumption. The XGBoost model could be effectively used to predict the effluent of the proposed AOAOCA. This study could provide a highly feasible reference about the treatment of WTS wastewater for practical application.

A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System

Background: Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing, edge computing is provided as a new paradigm to effectively reduce processing latency, energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems, an intelligent task migration assignment mechanism based on blockchain is proposed, which jointly considers the factors of resource allocation, resource control and credit degree. Methods: In this paper, an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay, energy consumption, communication, and credit degree. Here, the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes, and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge. Results: Compared to the existing two benchmark mechanisms, the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost. Conclusion: To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.

ENHANCING GREEN COMPUTING THROUGH ENERGY-AWARE TRAINING: AN EARLY STOPPING PERSPECTIVE

This study delves into energy-efficient training strategies, emphasizing their alignment with green computing principles. In particular, it highlights the utility of early stopping mechanisms in optimizing the training process of deep learning models. Early stopping works by monitoring performance metrics, such as validation accuracy or loss, and halting the training process once these metrics stabilize or show no improvement over a predefined number of epochs. This approach eliminates redundant computations, leading to significant reductions in energy consumption and computational costs while preserving model accuracy. The research is centered on transfer learning models, specifically MobileNetV2, InceptionV3, ResNet50V2, and Xception, which are well-regarded for their versatility and performance in image classification tasks. By systematically varying patient values (3, 5, 7, 10, and 15), the study explores their impact on training duration, model accuracy, and computational efficiency. Each patience value determines how many epochs the training continues without improvement before stopping, allowing for a nuanced examination of its effects across different architectures. The findings reveal that early stopping not only streamlines the training process but also aligns well with the broader goals of sustainable artificial intelligence development. By effectively balancing computational efficiency with performance optimization, this strategy exemplifies how environmentally responsible practices can be integrated into AI workflows. This study contributes valuable insights into how adopting such techniques can mitigate the environmental impact of AI model training, highlighting their importance in the context of advancing green computing initiatives.

A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop scheduling problem

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Enhancing scalable reconfigurable manufacturing systems through robust optimisation: energy efficiency and cost minimisation under uncertainty

Reconfigurable manufacturing systems are dynamic systems designed with scalable and flexible production capabilities to address changing market demands. This paper presents a novel multi-objective integer programming model aimed at optimising the configuration and capacity scalability of reconfigurable machine tools in uncertain environments. The model focuses on minimising three key objectives: total energy consumption, unused capacity, and total cost. It incorporates critical manufacturing constraints such as peak power thresholds and limited tool availability. To effectively manage uncertainty, particularly in demand fluctuations, a scenario-based robust optimisation approach is applied, striking a balance between solution robustness and model adaptability. A comprehensive case study demonstrates the model's effectiveness, comparing deterministic and uncertain solutions. Additionally, sensitivity analyses are performed on parameters such as peak power thresholds, risk coefficients, and infeasibility weights, highlighting their impact on system performance. The results provide insights into the efficient design and operation of scalable reconfigurable manufacturing systems under uncertainty, with recommendations for future research directions.

Advances in Surface-Enhanced Raman Spectroscopy for Therapeutic Drug Monitoring.

Therapeutic drug monitoring (TDM) is pivotal for optimizing drug dosage regimens in individual patients, particularly for drugs with a narrow therapeutic index. Surface-enhanced Raman spectroscopy (SERS) has shown great potential in TDM due to high sensitivity, non-destructive analysis, specific fingerprint spectrum, low sample consumption, simple operation, and low ongoing costs. Due to the rapid development of SERS for TDM, a review focusing on the analytical method is presented to better understand the trends. This review examines the latest advancements in SERS substrates and their applications in TDM, highlighting the innovations in substrate design that enhance detection sensitivity and selectivity. We discuss the challenges faced by SERS for TDM, such as substrate signal reproducibility and matrix interference from complex biological samples, and explore solutions like digital colloid-enhanced Raman spectroscopy, enrichment detection strategies, microfluidic SERS, tandem instrument technologies, and machine learning-enabled SERS. These advancements address the limitations of traditional SERS applications and improve analytical efficiency in TDM. Finally, conclusions and perspectives on future research directions are presented. The integration of SERS with emerging technologies presents a transformative approach to TDM, with the potential to significantly enhance personalized medicine and improve patient outcomes.

Additive Manufacturing of a Frost-Detection Resistive Sensor for Optimizing Demand Defrost in Refrigeration Systems.

This article presents the development of a resistive frost-detection sensor fabricated using Fused Filament Fabrication (FFF) with a conductive filament. This sensor was designed to enhance demand-defrost control in industrial refrigeration systems. Frost accumulation on evaporator surfaces blocks airflow and creates a thermal insulating barrier that reduces heat exchange efficiency, increasing energy consumption and operational costs. Traditional timed defrosting control methods can mitigate these effects but often lead to inefficiencies due to their inability to align with actual frost accumulation, which can vary according to system and environmental conditions. Frost-detection sensors aim to solve this problem by acting as a tool to support defrosting control. A series of tests were conducted to evaluate the sensor's performance in detecting frost under controlled conditions on a heat exchanger (HX). The sensor reliably detected frost in all cases, demonstrating its effectiveness in real-time frost detection. The sensor measurements were validated by comparison with results obtained through a computer vision method, confirming its reliability. It was also found that the sensor can detect temperature changes. This advancement in sensor technology highlights the potential of additive manufacturing to provide cost-effective, customizable, replicable, and compact sensor designs, contributing to improved system performance and energy efficiency in refrigeration systems.

An agile multimodal microrobot with architected passively morphing wheels.

Multimodal microrobots are of growing interest due to their capabilities to navigate diverse terrains, with promising applications in inspection, exploration, and biomedicine. Despite remarkable progress, it remains challenging to combine the attributes of excellent maneuverability, low power consumption, and high robustness in a single multimodal microrobot. We propose an architected design of a passively morphing wheel that can be stabilized at distinct geometric configurations, relying on asymmetric bending stiffness of bioinspired tentacle structures. By integrating such wheels with electromagnetic motors and a flexible body, we develop a highly compact, lightweight, multimodal microrobot (length ~32 mm and mass ~4.74 g) with three locomotion gaits. It has high motion speed (~21.2 BL/s), excellent agility (relative centripetal acceleration, ~206.9 BL/s2), low power consumption (cost of transport, ~89), high robustness, and strong terrain adaptabilities. Integration of batteries and a wireless control module enables developments of an untethered microrobot that maintains high motion speed and excellent agility, with capabilities of traveling in hybrid terrains.

Fuel consumption analysis and cost overview of mechanical vehicles in petroleum drilling rig

During the drilling activities carried out before the use of Petroleum and Geothermal Energy resources, daily fuel consumption occurs depending on the operational diversity of the mechanical components of different types of rigs in our country. In this study, a time-dependent financial analysis of the preferred operating procedure and fuel consumption during drilling operations was performed, both in engineering studies and in order to reach a predictable level in terms of cost, oil drilling operations were mentioned by drilling rig type today, and comparisons of mechanical evenings were made to provide an economically predictable earning environment for daily fuel consumption. As a result of the research and findings, Group Motors/Drawworks, Pump Motor and Generators were actively used during the idling of mechanical evenings, and the average daily diesel consumption of 23.61 lt/h, 45.8 lt/h and 33.3 lt/h was reached, respectively. Maneuver Opr. the average daily diesel consumption of 37.5 lt/h, 0 lt/h and 39.58 lt/h was reached based on the active use of Group Motors/Drawworks, Pump Motors and Generators during the operation. Drilling Opr. the average diesel consumption of 26.24 lt/h, 129 lt/h, 33.3 lt/h and 83 lt/h respectively was reached based on the active use of Group Motors/Drawworks, Pump Motor, Generators and Top Drive.

Multi-objective optimal energy-efficient retrofit determination using hybrid urban building energy model: Considering uncertainties between models

AbstractTypical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty. To solve it, this study proposes a decision support framework that integrates a hybrid urban building energy model (UBEM) method, NSGA-II, and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty. The study took the building groups in Sipailou campus as a case study and identified 76 “stable solutions” and 149 “active solutions” that minimize energy consumption, carbon emission, and life-cycle cost (LCC) over 30 years from 40,353,607 retrofit schemes. Key findings include that when considering model uncertainty, the quantities, types, and ranks of optimal retrofit solutions have changed. When the error of baseline UBEM validation is within ±5% and considering uncertainty transmission from energy simulation to ANN model, the energy-saving potential of optimal retrofit schemes has expanded from [63.78, 65.05]% to [60, 68.75]%, carbon-saving potential has shifted from [63.69, 64.09]% to [59.92, 67.79]%, and the LCC has changed from [−40.68, 14.59] × 106 to [−38.25, 16.97] × 106 Yuan. This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.

Low‐Temperature Cured and High‐Toughness Epoxy Resin Modified by Triblock Liquid Rubber

ABSTRACTLow‐temperature cured epoxy resin (EP) is a suitable resin system for bonding thermal and temperature‐sensitive devices, which can also reduce energy consumption and production costs. In order to further broaden the application range of low‐temperature curing EP systems in amine‐sensitive environments, in addition to selecting a well‐compatible curing agent, an effective rubber to form a three‐dimensional network structure with the resin matrix, so as to significantly improve the toughness of the EP. In this work, two kinds of triblock liquid rubber hydroxy‐terminated polypropylene glycol‐polybutadiene‐polypropylene glycol (HTPO‐PB‐PO) and hydroxy‐terminated polyphenylethylene oxide‐polybutadiene‐polyphenylethylene oxide (HTSO‐PB‐SO) with controlled molecular weight are synthesized and adopted to improve the mechanical properties of cured EP with methyl hexahydro phthalic anhydride (MEHHPA). It is found that all triblock liquid rubber‐modified epoxy systems can be cured below 100°C, and the curing mechanism is similar to that of the pure EP system. Compared with the original pure EP, the impact resistance of modified samples is significantly improved, and the glass transition temperature (Tg) is similar, among which the best impact strength of modified EP can reach 16.22 kJ/m2, 62.7% higher than that of the pure sample with only slightly reduced thermal decomposition temperature. These results can provide a low‐temperature curing resin material with excellent mechanical properties, high dimensional accuracy, and high heat resistance for amine‐sensitive environments while reducing costs due to high‐temperature components and energy consumption.

Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.

Green manufacturing electrical network scheduling using Slime Mould and Interior search algorithms

The strategic reduction of power consumption in electrical machines, particularly within manufacturing networks, is critical for achieving energy efficiency and sustainability. Bio-inspired algorithms have demonstrated remarkable potential for optimization, with their adaptability to specific problem domains being a key factor in their success. This study employs discrete optimization algorithms tailored for scheduling in an electrical manufacturing network modeled as a hybrid flow shop, where energy efficiency is paramount. Experimental evaluations were conducted on a customized hybrid flow shop benchmark comprising approximately 45 cases and 900 instances, utilizing a random distribution of hourly energy consumption and a specific tariff structure for monthly usage. The findings demonstrate the efficacy of the proposed discretized metaheuristics in significantly reducing power consumption costs while maintaining a minimized total makespan. This work highlights the potential of bio-inspired algorithms for green hybrid flow shop scheduling within electrical networks, contributing to enhanced operational efficiency, sustainability, and energy conservation in modern manufacturing environments.

Carbon-Aware Demand Response for Residential Smart Buildings

The stability and reliability of a smart grid are challenged by the inherent intermittency and unpredictability of renewable energy as its integration into the smart grid increases. This places enormous pressure on the smart grid to manage high loads and volatility. To effectively mitigate the impact of new energy integration on smart grids, demand response (DR) can be altered to the demand-side burdens. Using residential smart buildings (RSBs) in Shanghai, this study proposes a carbon-aware demand response (CADR) model that is predicated on the coordination of power carbon intensity and real-time electricity prices. In order to accomplish a more comprehensive reduction in overall electricity consumption costs, we conducted real-time scheduling of a building’s electrical devices using a greedy algorithm. In addition, a model of an optimal charging and discharging scheme for household electric vehicles was established, which is based on various charging modes, taking into account the electrification of the transportation sector. The cost of EV charging is reduced by an average of 23.18% and 33.2% under the two common charging modes, while the integrated cost of the total annual electricity consumption of household devices is reduced by 8.69%, as indicated by the simulation results.

A LINEAR PROGRAMMING APPROACH TO OPTIMIZING ENVIRONMENTAL RESOURCE MANAGEMENT IN URBAN AREAS

This study investigates the optimization of environmental resource management in urban settings using a linear programming approach, focusing on ammonia production and wastewater management. The model addresses uncertainties in resource allocation by evaluating the cost implications of wastewater discharge during production. Key variables include water drawn from rivers, sludge discharge, and energy consumption. The study explores scenarios where zero-discharge policies are implemented, resulting in reduced water usage but increased energy consumption and higher production costs. The methodology employed linear programming to minimize the cost of ammonia production while ensuring water quality through regulated waste disposal. The findings indicate that minimizing river impurities through controlled waste discharge reduces water usage but escalates energy consumption, complicating cost management. Results from the numerical example show that under optimal conditions, 6,666.7 liters of water and 555.6 kg of sludge are needed to produce 41,666.7 kg of ammonia, with zero energy consumption in the process. The significance of these results lies in their potential to inform sustainable urban resource management policies that balance economic and environmental priorities. The study concludes that while achieving high water quality increases costs, it is crucial for sustainable ammonia production and environmental conservation, particularly in minimizing the ecological impact of industrial activities on water resources.