Cost Efficiency Research Articles

Intelligent optimal sitting and sizing of distributed resources in presence of renewable energy and fuel cells based on reliability and emission factors

As the integration of renewable energy sources—such as wind, solar, and fuel cells—into power systems increases, challenges related to system reliability and cost efficiency become paramount. This paper examines the reliability of a standard power network composed of diverse energy sources including wind turbines, solar panels, fuel cells, batteries, and diesel generators. This paper proposes a novel hybrid optimization approach utilizing a Grey Wolf Optimizer combined with another advanced algorithm to minimize the total cost of energy production, inclusive of emission penalties for CO2, NO2, and SO2. The proposed approach is tested on IEEE 57-bus and IEEE 118-bus systems, demonstrating significant improvements over existing algorithms. Quantitative results show that our method reduces the total cost by up to 18 % and enhances system reliability by reducing voltage deviations in various scenarios by 12 %. The paper also provides insights into the optimal sizing and placement of wind turbines, photovoltaics, diesel generators, and fuel cells under different emission constraints (15000, 25000, and 45000 kg), further emphasizing the flexibility and effectiveness of our method in addressing environmental and economic challenges in modern power systems.

Optimal reserve policies for emergency medical supplies: Joint consideration of reserving safety stocks, production capacity, and capital

The tragic consequences of the COVID-19 epidemic highlight to governments the importance of keeping sufficient medical supplies. Given that the physical stocks are subject to considerable obsolescence risk, existing studies have considered combining holding safety stocks with keeping production capacity or capital reserve. Pioneering the exploration of keeping all three resources of safety stocks, production capacity, and capital in reserve, we derive closed-form optimal reserve policies under different scenarios. One crucial insight is that the optimal reserve policies should be determined by considering both the cost and utilization efficiency, e.g., solely adopting safety stocks is optimal when the utilization efficiency of safety stocks exceeds that of production capacity and capital reserves, and when the expected benefit of utilizing safety stocks outweighs the cost. Additionally, we find that the optimal safety stocks level is impacted by the combination of demand uncertainty and epidemic outbreak probability, i.e., the optimal safety stocks level decreases (resp., increases) with demand uncertainty in case with a sufficiently low (resp., high) epidemic outbreak probability, whereas the optimal levels of production capacity and capital always increase with demand uncertainty. We show that coordinating the three resources brings extra benefits compared with keeping fewer resources. By exploring the practical scenario where the government faces multiple potential epidemics, we find that below a threshold, a lower outbreak probability of a large-scale epidemic leads to more capital and production capacity reserves.

Highly-selective Electrocatalytic Reduction of NO to NH3 using Cu Embedded WS2 Monolayer as Single-atom Catalyst: A DFT Study.

Electrocatalytic nitric oxide reduction reaction (NORR) is a promising method for generating NH3. However, developing a NORR catalyst for NH3 synthesis with low cost and high efficiency is still challenging. We here report a series of single-atom catalysts (SACs), designed by embedding nine different transition metals from Sc to Cu in S-vacant WS2 monolayer (TM@WS2), and investigate the electrocatalytic performance for NORR process using the dispersion-corrected density functional theory (DFT) calculations. Among them, Cu-based SACshows a strong binding to the WS2 surface and high selectivity towards NORR process, and also it greatly inhibits the competing hydrogen evolution reaction (HER). Through ab initio molecular dynamics (AIMD) simulations, the thermal stability of SAC is assessed and found no structure deformation even at 500 K temperature. All possible reactive pathways including distal and alternating mode at both N- and O-end configurations for NH3 production were explored. We predicted that the Cu@WS2 SAC exhibits remarkable catalytic activity and selectivity with lowest limiting potential of -0.41 V via the N-alternating pathway. Our study emphasize that the transition metal dichalcogenide (TMDC) based SACs are potential candidates for converting NO to NH3, and this opens a new avenue in designing NORR catalysts with high catalytic performance.

Trifluoromethoxylation of Arynes Using 2,4‐Dinitro‐1‐(trifluoromethoxybenzene) as Trifluoromethoxide Anion Source

AbstractThe trifluoromethoxy moiety, an emerging fluorinated group, shows interesting properties for pharmaceutical and agrochemical applications. However, only few methods have been reported for the direct introduction of this group on molecules with high efficiency, safety and low cost. Indeed, most of direct trifluoromethoxylation reagents are either expensive, hazardous or require preliminary synthesis before their use. Here we describe the use of 2,4‐dinitro‐1‐(trifluoromethoxy)benzene (DNTFB) as an easy‐to‐handle, safe, commercially available and cheap source of trifluoromethoxide anion for the trifluoromethoxylation of arynes.

Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

Reduction potentials of chemical-synthetic pesticides – A case study using the example of an Eco-Scheme in southern Germany

Chemical-synthetic pesticides (CSPs) are associated with several negative environmental effects. The European Commission aims to reduce their use by 50% by 2030. This paper looks at the German Eco-Scheme, an agri-environmental payment scheme, for the foregoing of chemical-synthetic pesticides (CSPs) in arable crops for a one-year period. Using Baden-Württemberg in southern Germany as a case study, we investigated the suitability of the Eco-Scheme as a policy tool for CSP reduction. We used a field-based, georeferenced integrated land use model based on linear programming. Different payment levels for the Eco-Scheme were simulated. In addition, we analyzed the effect of labor availability, crop yield requirements, and market prices on Eco-Scheme implementation (uptake).The implementation potentials at €130/ha, the payment level in 2023, were limited and strongly dependent on market prices as well as potential food supply targets. The percentage decline in overall CSP use (measured in active substance mass) was even lower than the implementation in the percentage of acreage attributed to the Eco-Scheme. The implementation potentials were mainly observed on marginal sites. Higher simulated payment levels led to lower cost efficiency and dead weight losses. This trade-off between cost efficiency and effective reduction questions the suitability of the Eco-Scheme, at least for more substantial CSP reductions. To increase economic efficiency, we recommend differentiating payments based on abatement costs, for example on a crop-basis.

A Heterogeneous Acid-Base Organocatalyst For Cascade Deacetalisation-Knoevenagel Condensations.

Multifunctional heterogeneous catalysts are an effective strategy to drive chemical cascades, with attendant time, resource and cost efficiencies by eliminating unit operations arising in normal multistep processes. Despite advances in the design of such catalysts, the fabrication of proximate, chemically antagonistic active sites remains a challenge for inorganic materials science. Hydrogen-bonded organocatalysts offer new opportunities for the molecular level design of multifunctional structures capable of stabilising antagonistic active sites. We report the catalytic application of a charge-assisted, hydrogen-bonded crystalline material, bis(melaminium)adipate (BMA), synthesised from melamine and adipic acid, which possesses proximate acid-base sites. BMA exhibits high activity for the cascade deacetalisation-Knoevenagel condensation of dimethyl acetals to form benzylidenemalononitriles under mild conditions in water; BMA is amenable to large-scale manufacture and recycling with minimal deactivation. Computational modelling of the melaminium cation in protonated BMA explains the observed catalytic reactivity, and identifies the first demethoxylation step as rate-limiting, in good agreement with time-dependent 1H NMR and kinetic experiments. A broad substrate scope for the cascade transformation of aromatic dimethyl acetals is demonstrated.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Simultaneously enhancing the flow properties and mechanical strength of polyphenylene sulfide via in‐mold ultrasonic‐assisted injection molding

AbstractInjection molding is widely used for making intricate plastic products due to its precision, cost efficiency, and fast production. Industries like consumer electronics, aerospace, and medical, demand high mechanical properties from these parts. However, high‐performance polymers face challenges in flowability. Ultrasonic‐assisted injection molding offers a solution, but its impact on flowability and mechanical properties is not fully understood. We propose a novel injection mold design with in‐mold ultrasonic vibration to address this. By applying high‐frequency shear forces to the melt, it alters polymer chain alignment, enhancing flow and microstructure. Employing polyphenylene sulfide (PPS) resin as the primary material, this study systematically explores the impact of varying ultrasonic vibration durations and stages on melt filling behavior and product performance. Compared with conventional molds, ultrasonic‐assisted molds reduce melt viscosity by 38% and improve mechanical properties by 10.21%, with a 17.1% increase in crystallinity. This technology shows promise for improving flowability and mechanical properties in injection molding, with potential for wide industrial application.

Value-based preoperative assessment in a large academic hospital

BackgroundValue-based healthcare (VBHC) is an approach that focuses on delivering the highest possible value for patients while driving cost efficiency in health services. It emphasizes improving patient outcomes and experiences while optimizing the use of resources, shifting the healthcare system’s focus from the volume of services to the value delivered. Our study assessed the effectiveness of implementing a VBHC-principled, tailored preoperative evaluation in enhancing patient care and outcomes, as well as reducing healthcare costs.MethodsWe employed a quality improvement, before-and-after approach to assessing the effects of implementing VBHC strategies on the restructuring of the preoperative evaluation clinics at Humanitas Research Hospital. The intervention introduced a VBHC-tailored risk matrix during the postintervention phase (year 2021), and the results were compared with those of the preintervention phase (2019). The primary study outcome was the difference in the number of preoperative tests and visits at baseline and after the VBHC approach. Secondary outcomes were patient outcomes and costs.ResultsA total of 9722 patients were included: 5242 during 2019 (baseline) and 4,480 during 2021 (VBHC approach). The median age of the population was 63 (IQR 51–72), 23% of patients were classified as ASA 3 and 4, and 26.8% (2,955 cases) were day surgery cases. We found a considerable decrease in the number of preoperative tests ordered for each patient [6.2 (2.5) vs 5.3 (2.6) tests, p < 0.001]. The number of preoperative chest X-ray, electrocardiogram, and cardiac exams decreased significantly with VBHC. The length of the preoperative evaluation was significantly shorter with VBHC [373 (136) vs 290 (157) min, p < 0.001]. Cost analysis demonstrated a significant reduction in costs, while there was no difference in clinical outcomes.ConclusionsWe demonstrated the feasibility, safety, and cost-effectiveness of a tailored approach for preoperative evaluation. The implementation of VBHC enhanced value, as evidenced by decreased patient time in preoperative evaluation and by a reduction in unnecessary preoperative tests.

SIMULATION ANALYSIS TO FORECAST PROGRESS AND OPTIMIZE RESOURCE UTILIZATION FOR MATERIAL TRANSPORTATION ACTIVITIES IN THE CONSTRUCTION PROJECT ON PHU QUY ISLAND

This article presents the main results of using a simulation tool to predict time and optimize the resource utilization for the transportion of materials processes in the construction project on Phu Quy island. A simulation model, which includes activities of material transfer processes, has been established and developed based on the EZStrobe simulation program. Various simulation scenarios with different resource usage options have been analyzed. The simulation results are also compared and evaluated with real-world project data. It indicates that for both productivity, time and cost, the option of using two 50-ton cranes at the dock; two 25-ton cranes at the storage yard and four trucks will be the best solution. The results of the study demonstrated the effectiveness of the simulation program, which may help managers choose the resource allocation plan and apply necessary changes to obtain the optimum results in terms of production and cost efficiency for the transportion of contruction materials on the island.

An Overview of Cloud Computing Systems

Cloud computing has revolutionized IT resource management, providing scalable and flexible internet services that significantly differ from traditional on-premises infrastructure. This paper presents a comprehensive overview of cloud computing systems, examining their architecture, service models, deployment models, and the advantages and challenges they pose. The architecture of cloud computing is foundational, comprising client-side devices, cloud infrastructure backend, and networking components. Service models are categorized into Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), each offering different levels of abstraction and control. Deployment models include public, private, hybrid, and community clouds, each tailored to specific organizational needs. The advantages of cloud computing, such as cost efficiency, scalability, accessibility, disaster recovery, and innovation, are significant. However, challenges like security and privacy concerns, downtime and reliability issues, compliance requirements, and vendor lock-in necessitate careful planning and management. Understanding these facets is crucial for organizations to effectively leverage cloud computing and navigate its complex landscape.

The Influence of Employee Commitment on the Performance of State Departments in Kenya

The Public Service in Kenya is encountering numerous problems that demand attention to achieve its maximum capability of delivering high-quality services to citizens. The study is to assess the influence of employee commitment on performance of state departments in Kenya. Data obtained was then analysed through descriptive statistics, frequencies and correlational analysis, that the correlation between variables organisational commitment and organisational performance is strong and significant (r = 0.610, p &lt; 0.01), indicating a positive relationship and the findings then presented through tables, bar charts, and histograms. Research limitations are constraints of time and difficulty in gathering information from state department officers. The results revealed that employee commitment improves productivity, service delivery, cost efficiency, and a positive work environment in state departments. Thus, the state departments should implement are to Strengthen Leadership and Management, Employee Engagement, Improve Working Conditions, Promote Employee Well-being, and Implement Fair Policies and Practices.

India's Use of Artificial Intelligence in Healthcare

By completing tasks that would normally be completed by people in a fraction of the time and at a fraction of the expense, artificial intelligence makes life easier for patients, physicians, and hospital managers. AI is used in healthcare in a variety of ways, including the discovery of new genetic connections, the operation of robots that assist during surgery, the automation of administrative processes, the customization of treatment plans, and much more. India has witnessed an exponential rise in the field of artificial intelligence (AI). A revolution in cost reduction, efficiency, quality, and accessibility of healthcare services for millions of users is anticipated with the integration of artificial intelligence into the healthcare system. The focus of the majority of AI-based proposals in India, according to the literature currently accessible on AI and healthcare systems, has been to provide AI-based medical services to underserved remote rural communities where people cannot afford high-quality medical facilities.

Ground Source Heat Pumps in Buildings Revisited and Prospects

A large number of ground-source heat pump (GSHP) systems have been used in residential and commercial buildings throughout the world due to their attractive advantages of high energy and environmental performances. In particular, GSHPs constitute a proven renewable energy technology for space heating and cooling. This paper provides a detailed literature review of the primary aspects of GSHP systems. These include the technological characteristics of HPs and the main types and variations in GSHPs, along with their environmental impact. Other aspects addressed are the integration of GSHPs with other systems, as well as their optimal design and control and energy analysis. The important aspect of the system’s performance is also dealt with through case studies and also the barriers hindering the further adoption of GSHPs in buildings. Two important challenges for the adoption of GSHPs is their cost and environmental efficiency. Studies have shown that GSHPs can reach a &gt;&gt;24% lower environmental impact than air-source HPs, while today’s technology can allow for a payback period for installing a GSHP of &lt;&lt;5 years. Finally, based on the above review, the future challenges and prospects for the successful uptake of GSHPs is discussed. It seems that through the right steps, the wide adoption of GSHPs as an important form of ‘implemented’ renewable energy system can become a reality.

Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0

Anomaly detection for industrial diesel generators, in which unexpected faults could lead to severe consequences, is still challenged due to their complex structure and nonstationary operation. Maintenance engineers who manually audit diesel generators for anomaly detection require significant expertise and knowledge. This study proposes a real-time intelligent AIoT system-based convolution neural network long short-term memory (CNN-LSTM) to enhance efficiency and decrease labor costs of industrial diesel generator maintenance service. The AIoT system could autonomously classify abnormal conditions of industrial diesel generators through supervised learning techniques. Several anomaly failure conditions are identified by maintenance experts and are simulated in the laboratory to collect the working parameters based on developed IoT modules. Pearson product-moment coefficients are computed to effectively evaluate the interdependence between collected variables and the target anomaly types. The proposed CNN-LSTM structure is hyperparameter fine-tuning for identifying the most critical configurations in failure-diagnosing applications. The developed approach is comprehensively analyzed and evaluated with other state-of-the-art individual deep learning algorithms, including recurrent neural network (RNN), LSTM, gate-recurrent unit (GRU), and CNN. The experiment results indicate that the proposed hybrid CNN-LSTM could achieve distinguished diagnosis precision of anomaly conditions of industrial diesel generators and significantly improve the classified performance in Industry 4.0.

Analysis of the Application of Hollow Core Slab to Cost and Time Efficiency in High Rise Building Construction Project

The floor slab is one of the factors that affect the cost and duration of construction projects because it has the largest volume of work among other jobs. The selection of the method of implementation of floor slab work also affects the efficiency of development. The use of precast slabs can be an alternative to conventional floor slabs because of their potential to increase cost efficiency, speed up implementation time, and increase structural durability. In this study, a comparative analysis of cost efficiency and scheduling time of precast hollow core slab (HCS) method as an alternative to conventional slabs was conducted, using primary data obtained from the project and in accordance with government regulations. Time analysis and scheduling were calculated with the help of Microsoft Project software and determination of plate work items. As for the cost analysis, it is calculated based on the literature and applicable regulations to get real results by field conditions. Based on the analysis, it is known that the implementation of the 4-storey building slab work with the alternative precast slab method takes 8 days and costs Rp. 1,691,887,497. While the conventional slab method takes 38 days with a total cost of Rp. 2,276,920,601.

An integrated approach to green power, cooling, and freshwater production from geothermal and solar energy sources; case study of Jiangsu, China

This paper introduces an innovative integration of geothermal and solar energies to assess the production of power, cooling, and freshwater. These products are achieved through a modified absorption refrigeration cycle, a modified organic Rankine cycle, and humidification-dehumidification subsystems driven by a flash-binary configuration. The performance of the setup is evaluated using both thermodynamic and economic approaches. Additionally, three multi-objective optimizations are applied to determine a suitable optimal state. The approaches reveal net power, cooling, and freshwater production rates of 807.3 kW, 1345 kW, and 7.96 kg/s, respectively. The designed setup requires a total capital investment cost of $2,490,310 and has a payback period of 5.76 years in the base mode. The study highlights that the temperature difference of superheating significantly impacts the total capital investment cost and exergy efficiency, while the cooling production is mainly influenced by the evaporator temperature. The final optimal state achieved an exergy efficiency of 15.98 % and a net present value of $1,999,864. Overall, this innovative hybrid geothermal-solar setup demonstrates a viable solution for sustainable energy production, combining power generation, cooling, and freshwater production in a single integrated system. The findings support the system's potential for real-world applications, particularly in regions with suitable geothermal and solar resources.

Performance of a high-temperature transcritical pumped thermal energy storage system based on CO2 binary mixtures

Pumped thermal energy storage is a novel energy storage technology with features of high efficiency, geographical independence and suitable for bulk capacity energy storage. As a subset of pump thermal energy storage system, the transcritical CO2 arrangements have received widespread attention due to their excellent thermodynamic performance. However, the high cost and low efficiency of heat exchange progress caused by CO2 phase transition hinder the transformation of this system from scientific theory to practical application. CO2 binary mixtures is innovatively employed as working fluid in proposed system. The temperature of mixture is variable rather than remaining constant during the evaporation and condensation progresses. R134a, R290, R600 and R601 are selected respectively to mix with CO2 to form the new working fluids. Advanced thermodynamic and economic models are established to investigate the performance of novel systems. Results indicate that the system driven by CO2/R134a with a refrigerant mass fraction of 0.15 exhibits the optimal overall performance. At the optimal point of the 10 MW/8h system, the round trip efficiency and levelized cost of storage are 66.18 % and 0.146 $/kWh, respectively. Moreover, the investigation of system performance with various energy storage capacities is put forward. It is shown that when the energy system capacity reaches 300 MW, the levelized cost of storage can be reduced to 0.122 $/kWh. To adapt to different peak and valley periods in different regions, systems with different charge and discharge times are investigated and it is found that the charge duration should better be equal to the discharge duration.