Efficiency Improvement Research Articles

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

Graph neural network based human detection in videos during occlusion environments

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.

A Reduced Order Schwarz Method for Nonlinear Multiscale Elliptic Equations Based on Two-Layer Neural Networks

Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts, including solution of partial differential equations (PDEs). We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition, an accelerated Schwarz framework, and two-layer neural networks to approximate the boundary-to-boundary map for the subdomains, which is the key step in the Schwarz procedure. Conventionally, the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain. By leveraging the compressibility of multiscale problems, our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map. Our method is applied to a multiscale semilinear elliptic equation and a multiscale $p$-Laplace equation. In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.

Robot navigation on inclined terrain using social force model

<p><span lang="EN-US">This research introduces an innovative approach to address the limitations of the commonly used social force model-based robot navigation method on flat terrain when applied to sloped terrain. The incline of the terrain becomes a crucial factor in calculating the robot’s steering output when navigating from the initial position to the target position while avoiding obstacles. Therefore, we propose a social forced model-based robot navigation system that can adapt to inclined terrain using inertial measurement unit sensor assistance. The system can detect the surface incline in real time and dynamically adjust friction and gravitational forces, ensuring the robot’s speed and heading direction are maintained. Simulation results conducted using CoppeliaSim show a significant improvement in speed adjustment efficiency. With this new navigation system, the robot can reach its destination in 59.935089 seconds, compared to the conventional social forced model which takes 63.506442 seconds, the robot is also able to reduce slip to reduce wasted movement. This method shows the potential of implementing a faster and more efficient navigation system in the context of inclined terrain.</span></p>

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

Energy efficiency investment in Swiss industry: Analysis of target agreements

Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.

Light Load Efficiency Improvement for Three-Phase Inverter Employing Valley Switching Under Mixed TCM/DCM Operation

Light Load Efficiency Improvement for Three-Phase Inverter Employing Valley Switching Under Mixed TCM/DCM Operation

Explaining the environmental efficiency capability of energy mix innovation among the Nordic countries

Improving the environmental efficiency performance of energy mix is important to the global carbon neutrality goal. Given this motivation, this study delves into providing more explanation about the role of innovation approaches to coal, oil, and natural gas energy resources in the Nordic economies i.e., Denmark, Finland, Norway, and Sweden during the period 2000 to 2019. To achieve this objective and reach an evidence-based insight for policy formulation, the present study leveraged robust panel econometrics estimators that circumvent cross-sectional dependence and heterogeneity issues in the investigated blocs. The empirical evidence shows that economic growth (GDP), coal energy, oil and natural gas contribute to the Nordic economies' environmental degradation in the short- and long-run. Contrarily, innovation via environmental-related patents mitigates environmental degradation, especially in the long run. Furthermore, the interaction models, i.e., applying innovative approaches in the energy mix's consumption, can mitigate carbon emissions across the countries, thus significantly improving environmental sustainability. Specifically, innovative approaches in coal, oil, and natural gas consumption improve environmental sustainability by mitigating carbon emissions, thus delivering respective environmental efficiency improvements of ~94%, ~108%, and ~123%. Additionally, Granger causality evidence further suggests that the examined factors are significant drivers of the environmental indicator. Regarding policy insight, this study affirms that further improvement in energy savings is vital to continuously deploying the conventional energy mix.

The efficiency improvement for traffic operating and modifying delay time in al-Kadessah intersection at AL Ramadi city

The efficiency improvement for traffic operating and modifying delay time in al-Kadessah intersection at AL Ramadi city

Literature Review on Experimental Analysis of Domestic Refrigerator

Abstract: This literature review paper delves into the experimental analysis of the performance of domestic refrigerators, a cornerstone of modern household appliances. The review synthesizes a broad spectrum of studies that examine the efficiency, energy consumption, cooling capacity, and overall performance of various domestic refrigerator models. Key focus areas include the impact of different refrigerants, advancements in compressor technology, insulation materials, and the role of smart technologies in enhancing refrigerator performance. The review highlights experimental methodologies employed to assess refrigerator performance, such as standardized testing protocols, real-world usage simulations, and advanced diagnostic techniques. It also discusses the influence of external factors like ambient temperature, door opening frequency, and load variability on refrigerator efficiency. Comparative analyses within the literature reveal trends in energy efficiency improvements, driven by regulatory standards and consumer demand for environmentally friendly appliances. The integration of alternative refrigerants, such as R600a (isobutane), is examined for its environmental benefits and performance implications. The paper concludes by identifying gaps in current research and suggesting directions for future experimental studies. Emphasis is placed on the need for continued innovation in refrigeration technologies to meet growing energy efficiency and environmental sustainability requirements. Overall, this literature review provides a comprehensive overview of the experimental evaluation techniques and findings related to domestic refrigerator performance, offering insights that can guide future research and development in this vital field.

Enhancing U.S. workforce productivity through strategic data automation: Key insights and implications

The review explores how strategic data automation can enhance workforce productivity in the U.S., providing key insights and implications for organizations. As businesses increasingly rely on data-driven decision-making, the role of automation in streamlining processes and improving efficiency becomes paramount. This review delves into the potential benefits and challenges of implementing strategic data automation strategies and offers recommendations for organizations looking to enhance their workforce productivity. Strategic data automation has the potential to revolutionize how businesses operate, leading to significant improvements in efficiency and productivity. By automating repetitive tasks and streamlining data processing, organizations can free up valuable time and resources, allowing employees to focus on more strategic and value-added activities. However, the successful implementation of strategic data automation requires careful planning and consideration of various factors, including data security, privacy, and regulatory compliance. Key insights from this review include the importance of aligning data automation initiatives with organizational goals and objectives, as well as the need for ongoing monitoring and evaluation to ensure effectiveness. Additionally, the review explores the implications of strategic data automation for the future of work, highlighting the potential for increased collaboration between humans and machines and the need for new skills and competencies. Overall, this review provides valuable insights into how strategic data automation can enhance workforce productivity in the U.S. It highlights the benefits of automation, such as improved efficiency and reduced costs, while also addressing potential challenges and considerations for organizations. By leveraging the power of strategic data automation, organizations can position themselves for success in an increasingly data-driven world.

Analysis of dual active bridge-based on-board battery charger for electric and hybrid vehicles

This paper presents an in-depth analysis of a Gallium Nitride (GaN) Dual Active Bridge (DAB)-based on-board battery charger for electric and hybrid vehicles. It focuses on the superior efficiency and reliability of GaN-based power electronics over traditional Silicon-based systems. The study explores the implementation of the DAB topology, recognized for its bidirectional power flow and high efficiency, in enhancing vehicle charging infrastructure. Efficiency analysis, switching behavior and losses are thoroughly examined. Simulation results in PSIM software validate the theoretical predictions, showing significant improvements in operational flexibility and energy efficiency.

Technological advancements in drilling: A comparative analysis of onshore and offshore applications

This paper examines the technological advancements in onshore and offshore drilling operations, emphasizing their significance, challenges, and environmental considerations. Technological innovations, including automation, data analytics, and real-time monitoring, have transformed drilling practices, enhancing efficiency, safety, and environmental sustainability. By comparing onshore and offshore applications, the paper evaluates the effectiveness of these advancements in addressing the unique challenges of each environment. Key areas of focus include efficiency improvements, safety enhancements, environmental impacts, and regulatory compliance. Through case studies and analysis, the study aims to provide valuable insights into the current state of onshore and offshore drilling operations and identify opportunities for innovation and improvement. Ultimately, the findings contribute to advancing sustainable practices, mitigating environmental risks, and fostering responsible resource extraction in the oil and gas industry. This comparative study delves into the dynamic landscape of technological advancements within onshore and offshore drilling operations. It explores the evolving role of automation, data analytics, and real-time monitoring in revolutionizing drilling practices, offering insights into their implications for efficiency, safety, and environmental stewardship. By juxtaposing the unique challenges and operational requirements of onshore and offshore environments, the study underscores the importance of a nuanced understanding of technological applications. Through rigorous analysis and case studies, it illuminates the interplay between advancements and environmental considerations, shedding light on opportunities for innovation and sustainable development. The study's comprehensive scope encompasses efficiency gains, safety protocols, environmental impact mitigation, and regulatory compliance, providing a holistic view of the transformative potential of technology in the drilling industry. Ultimately, the study serves as a catalyst for informed decision-making, fostering dialogue, and driving forward-looking strategies for the future of onshore and offshore drilling operations.

Energy Efficiency Improvement for Manufacturing Companies in Latvia

Concerns about climate change and environmental issues are becoming more and more popular nowadays and are causing concerns in everyone's life and beyond. One of significant causes of climate change is energy consumption, especially in the manufacturing sector. Even though, energy efficiency (EE) topic has been extensively discussed, not all EE aims are met. For the manufacturing sector the reason is that EE is often not a top priority for the companies. Currently the companies are also faced with another big challenge – in this digital era of rapidly developing technologies they have to adjust their practices to seize the opportunities provided by digitalization and automatization. Therefore, the aim of this research is to explore how digitization can be combined with EE to promote the resilience and increase performance of manufacturing companies, and how remote data analysis, also by using machine learning tools, can help improve EE. Literature review, content analysis, empirical data analysis and cases studies from real production companies in Latvia are used to achieve the research goals. The results section shows how digital tools and real-time monitoring help assessing the current state of the business and make decisions on changes to future operations, helping to reduce consumption and the environmental impact of production. Manufacturing companies have the potential to improve EE through digitalisation, but the manufacturing processes are complex and Latvian companies are only slowly moving towards digitalisation. Therefore, there is a strong need to examine empirical cases to gather more perspective and to find a way to implement digitalization and EE improvement across all sectors. Conclusions suggest that manufacturing companies should be encouraged to move towards a more unified energy data collection system, enabling more efficient data analysis and proposals for energy efficiency improvements.

Experimentally thermal management of low-concentrated photovoltaic via new configurations of dimple/trapezoidal heat spreaders of different sizes

ABSTRACT This study experimentally investigates the influence of dimple (DHS), flat plate (FHS), and trapezoidal heat spreader (THS) cooling systems on thermal regulation of low concentrated solar cell (LCPV). It is performed at 1.5 and 2 HS/PV area ratio and HS thicknesses 1 and 2 cm under 3100 W/m2 solar irradiance. Results reveal that using DHS outperforms other cooling systems in terms of cell temperature and electric performance. Increasing HS area ratio and thickness significantly enhances PV performance. DHS, PHS, and THS achieve maximum PV temperature reduction of 44.4, 43.2, and 41°C and PV efficiency improvement of 16.89%, 14.87%, and 13.9%, respectively.

The differences between the hydrogenation by means of photon-injection and electron-injection for N-type tunnel oxide passivated contacts solar cells

Tunnel Oxide Passivated Contact (TOPCon) solar cells have received widespread attention in recent years, especially in improving conversion efficiency. This paper investigated the impact of hydrogenation technology using photon-injection (HPI) and electron-injection (HEI) processes on TOPCon solar cells, highlighting the higher improvement effect and broader application scope of HPI compared to HEI. In TOPCon cells, several methods are available to prepare the tunneling oxide layer, such as plasma oxidation (PO) and indirect thermal oxidation (TO). The research results indicated that significant improvement differences could be observed when utilizing the HEI treatment for TOPCon solar cells prepared by PO and TO methods, with values of 0.133%abs. and -0.039%abs., respectively. Meanwhile, HPI treatment induced a more significant efficiency improvement for these two types of cells, and the increase in efficiency is 0.247%abs. and 0.244%abs., respectively. The experimental results demonstrated that the passivation effect for TOPCon solar cells prepared by PO and TO methods remained almost the same under the HPI treatment, and the improvement effect is less dependent on the tunnel oxidation technique used. Thus, the different passivation effects between HPI and HEI were further investigated, and the reason for the difference was attributed to the charge states and concentrations of hydrogen and non-equilibrium carriers during the hydrogenation. The results provided an improved scheme for enhancing the efficiency of TOPCon solar cells, shedding light on the role of HPI and HEI in the passivation process. This work brings further insights to TOPCon solar cells.

Novel flow-electrode capacitive deionization system employing modified MOF-derived carbon electrodes for metal ion removal

Activated carbon (AC) as an electrode for capacitive deionization (CDI) is constrained by its low specific surface area and specific capacitance, leading to electrical double layer overlap (EDL). To address this issue, a flow electrode capacitive deionization system (MDC-FCDI) was developed, which used modified MOFs-derived carbon (MDC) as the electrode. The MDC electrode exhibited enhanced specific capacitance (145.78 F/g) and specific surface area (2458.44 m2/g). Compared to conventional FCDI, the MDC-FCDI system demonstrated a 35–39 % improvement in heavy metal removal efficiency from wastewater containing Pb2+, Cu2+, Cd2+, and Zn2+. The selectivity of MDC-FCDI for heavy metals exhibited a unique “two-step phenomenon” that differed from what was observed in conventional CDI systems. In the MDC-FCDI system, high-valent ions experienced stronger electric field forces and preferentially occupied active sites on the electrode surface prior to the adsorption of ions with lower valence. The selectivity of the MDC-FCDI system for heavy metal ions is determined by three factors in the following order of priority: ionic charge, hydration radius, and ion feed concentration. This study offers a practical approach for expanding the range of MDC-FCDI applications.

Multi-Faceted Analysis of Phase-Change Composite Intended for Autonomous Buildings

This paper presents the long-term, holistic results of research into an innovative heat accumulator based on an organic phase-change material in the form of a mixture of aliphatic alkanes, molecular silica sieves, carbon recyclate and epoxy and cement matrices. The research included chemical testing of vacuum soaking of molecular silica sieves with a liquid phase-change material. The results proved an improvement in the heat storage efficiency of the heat accumulators due to the addition of carbon recyclate by 28%, while increasing the heat storage time by 134 min, and a reduction in PCM leakage due to the use of molecular silica sieves. In addition to its cognitive scientific value, another research objective of the work achieved was to obtain response functions in the form of approximating polynomials. They provide a useful, validated and verified tool to predict the physical and chemical characteristics of heat accumulators with different contents of individual components. As part of the ongoing research, technical problems related to leak-proofing assurance and matrix selection for organic phase-change materials were also solved. The solution presented is in line with the issues of efficient use of renewable energy, low-carbon and energy-efficient circular economy.

Improvement of Climate Resource Utilization in the Southwestern Hilly Region through the Construction of a New Multi-Maturing Cropping System

The construction of an appropriate cropping pattern is crucial for the improvement of regional agricultural economic efficiency and sustainable development. Despite previous efforts, there remains a gap in optimizing cropping patterns that fully leverage climate resources to enhance production efficiency. This study addresses this gap by systematically comparing the differences in climate resource allocation, production efficiency and crop response among models by constructing four new triple-maturing cropping models at typical ecological sites in the hilly areas of southwest China. To solve the above problems, we constructed eight cropping patterns and classified them to three as follows: the Traditional Double Cropping System, the Traditional Triple Cropping System, the Novel Triple Cropping System. The results showed that the new multi-maturing planting pattern was significantly better than the traditional two-maturing netting pattern and the traditional three-maturing planting pattern in terms of light, temperature and water productivity. Compared with the traditional two-maturity net cropping model and the traditional three-maturity cropping model, the new cropping model increased light energy productivity by 97.88% and 50.00%, respectively; light energy use by an average of 0.48% and 0.31%; cumulative temperature productivity by an average of 84.70% and 49.14%; and rainfall productivity by an average of 101.04% and 49.61%. An assessment of the light, temperature and water meteorological resource use efficiency of the different treatments showed that the resource use efficiency of the new multi-maturing planting pattern was on average 111.58% and 74.78% higher than that of the traditional two-maturing net planting pattern and the traditional three-maturing planting pattern, with the T6 pattern having the highest resource use efficiency. The new multi-ripening cropping pattern has demonstrated production stability in response to changes in light, temperature and water resources, better adapting to weekly climate changes, stabilizing yields and improving efficiency. In summary, the results of this study can provide a theoretical basis for optimizing cropping patterns and promoting the use of climate resources in agriculture and sustainable development. Future research should focus on further refining these models, exploring their adaptability to various climatic conditions, and evaluating their long-term economic and environmental impacts.