Efficient Management System Research Articles

Producer responsibility organization how non-profit organizations

In connection with the adoption of the Law of Ukraine «On Waste Management», for the first time in Ukraine, such a special subject of these legal relations as an extended producer responsibility organization appears as part of the subjects of administrative legal relations. In Europe, these organizations have been operating for decades, while in Ukraine their activities are just beginning. In this regard, it should be noted that the role of the state, which exercises control over the activities of such business entities on its territory, is always quite important. This control and managerial influence on the activities of certain business entities often depends not only on the areas of their activities, but also on their organizational and legal forms, their profitability or non-profitability, which determines their tax status and forms a certain model of interaction with regulatory authorities. This article analyzes the approach of the Ukrainian legislator to the definition of extended producer responsibility organizations in Ukraine as non-profit organizations, and points out a number of legal issues that may arise in the future in connection with the functioning of the EPROs exclusively as non-profit organizations. It is also stated that the waste management legislation, as it exists today, is new for our country, as it is the result of harmonization of Ukrainian legislation with EU legislation, which, accordingly, provides for its adaptation to the specifics of the work of state bodies, existing business processes and business practices in Ukraine. This raises the need to address certain issues that might not otherwise have been raised. One of these issues is the non-profitability of RWMOs, which the author of the article considers to be the result of an unbalanced approach of the legislator to solving certain problems of RWMOs in Ukraine, and therefore proposes: amendments to certain legislative acts regulating the field of waste management in Ukraine, changing the status of RWMOs from non-profit to profit, which, in the author’s opinion, will allow to build a more efficient waste management system and achieve targets for waste recovery and recycling in Ukraine with the participation of organizations with extended responsibility.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Application of AI and IoT in Traffic Management of Large Metropolitan Cities

Urban traffic congestion is a growing problem worldwide, leading to wasted time, fuel, and increased emissions. To address this challenge, smart traffic management systems powered by the Internet of Things (IoT) and Artificial Intelligence (AI) are emerging as a promising solution. This paper explores how these technologies can be combined to create a more efficient and sustainable traffic management system for large cities.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks

Efficient cooling technology is crucial for high-temperature gas turbines which are significant applications in thermal processes particularly in industries such as power generation aerospace and marine propulsion. Enhancing cooling efficiency in these systems not only improves energy utilization but also plays a critical role in reducing fuel consumption increasing operational reliability and minimizing environmental impacts. This research focuses on optimizing the thermal performance of internal passage cooling channels with extended ribs using a combination of the response surface method and artificial neural networks. By varying the extended rib length and rib width the study aims to enhance heat transfer and minimize flow loss leading to improvements in overall system efficiency. Latin Hypercube Sampling is employed to strategically select variables and automated numerical simulations are conducted to perform a thorough parametric analysis. A steady-state k-ω shear stress transport turbulence model is applied in the simulations and the numerical results are validated against experimental data ensuring accuracy. The combined response surface method–artificial neural network approach integrated with TensorFlow-based modeling allows for the prediction of heat transfer and friction factor ultimately generating a comprehensive response surface to identify the optimal design point. Applying the optimized design leads to a 23.99 % improvement in the thermal performance factor compared to the reference case. The findings of this study highlight a significant enhancement in the thermal performance of the cooling channel structure particularly in high heat flux environments. This research provides valuable insights into the design of more efficient thermal management systems for gas turbines and has broader implications for other high-temperature thermal processes. The proposed optimization methodology offers a reliable approach for improving cooling technologies contributing to the development of the next generation of energy-efficient high-performance gas turbines and other industrial applications where effective thermal management is essential.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Content-Defined Chunking Algorithms in Data Deduplication: Performance, Trade-Offs and Future-Oriented Techniques

In the digital era, the exponential growth of data presents significant challenges for storage efficiency and processing speed. This paper reviews Content-Defined Chunking (CDC), a cornerstone in data deduplication technology, aimed at addressing these challenges. We systematically examine various CDC algorithms, categorising them into hashing-based and hash-less methodologies, and evaluating their performance in deduplication processes. Through a critical analysis of existing literature, the study identifies the balance between chunking speed and deduplication efficacy as a pivotal area for enhancement. Our findings reveal the need for innovative CDC algorithms to adapt to the evolving data landscape, proposing future research directions for improving storage and processing solutions. This work contributes to the broader understanding of data deduplication techniques, offering a pathway towards more efficient data management systems.

Aircraft Ducted Heat Exchanger Aerodynamic Shape and Thermal Optimization

Abstract Interest in aircraft electrification and hydrogen fuel cells is driving demand for efficient waste heat management systems. Ultimately, most of the heat must be rejected to the freestream air. Ducted heat exchangers, also called ducted radiators, are the most common and effective way to do this. Engineers manually design ducted heat exchangers by adjusting the duct's shape and heat exchanger's configuration to reduce drag and transfer sufficient heat. This manual approach misses potential performance improvements because engineers cannot simultaneously consider all of the complex interactions between the detailed duct shape, heat exchanger design, and operating conditions. To find these potential gains, we apply gradient-based optimization to a three-dimensional ducted heat exchanger computational fluid dynamics (CFD) model. The optimizer determines the duct shape, heat exchanger size, heater exchanger channel geometry, and coolant flowrate that minimize the ducted heat exchanger's power requirements while rejecting enough heat. Gradient-based optimization enables the use of nearly 100 shape design variables, creating a large design space and allowing fine-tuning of the optimal design. When applied to an arbitrary, poorly performing baseline, our method produces a nuanced and sophisticated ducted heat exchanger design with five times less cruise drag. Employing this method in the design of electric and fuel cell aircraft thermal management could uncover performance not achievable with manual design practices.

Investigation of the mechanical equivalent of heat using aluminum and brass cylinders

This study explores the mechanical equivalent of heat through controlled experiments using aluminum and brass cylinders. By mechanically rotating these cylinders against a friction band, the conversion of mechanical energy into heat is quantified, demonstrating a fundamental thermodynamic process. The experiment is designed to calculate the specific thermal capacities of the metals and evaluate the efficiency of energy transformation. Results validate the concept that mechanical energy, when converted through friction, becomes thermal energy—affirming the principles outlined in the conservation of energy. This work not only reinforces classical thermodynamics but also enhances our understanding of material properties under thermal stress, offering insights applicable to industrial applications and renewable energy technology. The findings underscore the practical implications of energy transformations in material science and engineering, contributing to the development of more efficient thermal management systems in various technological fields.

Plan for Improvement of Legal System for Efficient Management of Property for Public Use of Government Office Buildings of Central Administrative Agency: Focusing on Security Management under Legislative Bill Concerning Supply and Demand and Management of Government Office Building of Central Administrative Agency

Plan for Improvement of Legal System for Efficient Management of Property for Public Use of Government Office Buildings of Central Administrative Agency: Focusing on Security Management under Legislative Bill Concerning Supply and Demand and Management of Government Office Building of Central Administrative Agency

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The national system of energy saving management in budget sector organizations is presented through the prism of the implementation of targeted directions for increasing the level of energy efficiency by the program-target method, the purpose of which is the concentration of resources of business entities and the construction of a unified energy efficiency management system and the coordination of efforts of both state and regional authorities, local self-government, institutions,etc. An important role is given to the components of the program-target method as a mechanism of state management of a unified system of state and regional authorities and local self-government bodies, institutions, etc., which, in particular, will ensure the coordination of energy components and the formation of an acceptable price for educational services. According to the results of the research, the need for the formation of an energy-efficient policy in each institution of higher education, despite its regional location, based on a comprehensive approach from the standpoint of a single energy space, has been actualized. The author considers the management of energy saving projects of the budget sector organization in relation to their energy security system, i.e. the management system of a budget institution from the standpoint of a single energy space, which provides an effective, reliable and ecologically safe environment, which is minimally affected by internal and external factors of energy saving, and is the guarantor of the quality of services, in particular energy services, provided to the population by public sector facilities. The main factors of energy efficiency of budget sector organizations, the procedure for implementing the energy saving and energy efficiency improvement program, the main factors of managing energy efficient projects in the budget sector, analytical tools for modeling their evaluation system and the stages of developing a scenario of an innovative model of their management are presented. The proposed technologies and tools can be used to build an innovative management model of energy saving projects of budget organizations, in particular, using the pyramid-aggregate modeling method.

Estimation of state of charge for polymer solid-state batteries: Ensemble learning models and temperature impact study

In addition to the research and development of solid electrolytes to improve battery performance, an efficient battery management system (BMS) is a must to ensure safe use and extend battery life, and state of charge (SOC) estimation is critical in the BMS. This paper presents a novel temperature-influenced method for estimating the SOC of polymer-based solid-state batteries in real-time, using machine learning to capture the relationship between SOC and battery characteristics at different temperatures. The results show that accurate SOC estimation results can be achieved at different temperatures, with an average root mean square error of 1.42 %. Furthermore, the method uses Shapley Additive explanation (SHAP) to reduce the degree of black box nature of the model and analyzes the electrochemical processes inside the battery at different temperatures through relaxation time distribution. Accurate estimates of SOC and guidelines for appropriate operating temperatures can serve as a basis for BMS decision-making and improve the lifetime of polymer-based solid-state batteries.

Numerical simulation for comparison of cold plate cooling and HFE-7000 immersion cooling in lithium-ion battery thermal management

In this work, fluorinated liquids can be used as a new immersion coolant for effective battery thermal management (BTM) because of their insulating and non-flammable inert characteristics. In this research, the liquid chosen for immersion cooling is HFE-7000, characterized by a boiling point of 34 °C. The analysis of bubble kinetics is employed to investigate the heat transfer mechanism in two-phase immersion cooling by examining the behavior of bubbles during the boiling process. The square ternary Li-ion batteries are subjected under loads of 2C and 3C cooled by immersion cooling and a mini-channel cold plate as a comparison. It turns out that immersion liquid cooling modules offer superior thermal performance. Under constant current (CC), the peak temperature is reduced by 4.89 °C and 9.31 °C compared with the mini-channel cold plate cooling (CPC). Under dynamic load conditions, the maximum temperature rise of the battery is reduced by 29.89 % and 43.89 % compared to CPC. As a passive heat dissipation method, two-phase immersion cooling reduces the design requirements for active control. Also, it delivers an efficient battery thermal management system (BTMS) designed for upcoming electric vehicles (EVs).

Numerical Investigation on Enhanced Thermal Performance of Rectangular Microchannels with Triangular Ribs

Effective thermal management becomes crucial with the rising heat produced by electronic devices. Microchannel convection shows excellent potential as a cooling solution. This study employs numerical computational techniques to examine rectangular microchannels’ flow and heat transfer parameters on a semi-octagonal structure, specifically focusing on the Reynolds number (Re) ranging from 100 to 500. An extensive analysis evaluates the heat transfer design and hydraulic performance. Furthermore, the microchannel's diodic performance is assessed by comparing the Nusselt number (Nu) and the pressure drop in both the forward and backward directions. The study demonstrates the excellent performance of the microchannels within the microstructure under low Reynolds number (Re) conditions. Emphasizing the significance of preserving the fundamental geometric characteristics while improving the fluid dynamics within the microchannel. The semi-octagonal structure microchannel design surpasses the conventional design in bidirectional heat transfer by improving spatial fluid mixing. With a Reynolds number of 300, the Nusselt number of this microchannel surpasses that of the traditional rectangular design by a considerable margin. In addition, at this Reynolds number, the thermal diodicity (Dit) and pressure drop diodicity (Dip) showed significant improvements, with values of 1.16 and 1.45, respectively. Therefore, this study comprehensively compares the enhanced heat transfer of microchannel walls under constant heat flux, highlighting the advantages of semi-octagonal structural design in improving heat transfer and hydraulic performance. The results show that this innovative design has great potential to improve the efficiency of microchannel cooling and provides an important reference for the development of efficient thermal management systems.

Investigation and optimization of battery thermal management system based on composite phase change material and variable wall liquid cooling plate

To mitigate the risk of thermal runaway in lithium-ion batteries, an efficient battery thermal management system (BTMS) assumes paramount importance. A BTMS based on composite phase change material (CPCM) and variable wall liquid cooling plate (LCP) is proposed in this research. The numerical model of the BTMS was established and experimentally validated. The influence of the wall of LCP on battery temperature was investigated, and the efficiency of phase change material (EOP) index was proposed to assess the efficacy of CPCM. The genetic algorithm was employed to optimize the structure of the CPCM, and the influence of flow rate on the maximum temperature of the battery pack was studied. The results demonstrate a reduction of 1.81 °C in the maximum temperature of the battery pack upon implementation of the variable wall LCP. The optimized EOP achieves a value of 0.07 °C/g, resulting in a temperature difference of 0.56 °C. Furthermore, maintaining the maximum temperature of the battery pack below 40 °C only requires a water flow rate greater than 0.89 g/s. These results can serve as a valuable reference for the development of battery thermal management systems utilizing CPCM and liquid-cooling.

Evaluation of crop water stress index of wheat by using machine learning models.

The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops in the semi-arid region of western Uttar Pradesh, India, subjected to varying irrigation treatments across two cropping seasons (2021-2022 and 2022-2023). The aim is to investigate further the potential of four machine learning (ML) models-support vector regression (SVR), random forest regression (RFR), artificial neural network (ANN), and multiple linear regression (MLR) to predict CWSI. The ML models were assessed based on determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) under diverse scenarios created from eight distinct input combinations of six variables: air temperature (Ta), canopy temperature (Tc), vapor pressure deficit (VPD), net solar radiation (Rn), wind speed (U), and soil moisture depletion (SD). SVR emerges as the top-performing model, showcasing superior results over ANN, RFR, and MLR. The most effective input combination for SVR includes Tc, Ta, VPD, Rn, and U (R2 = 0.997, MAE = 0.901%, RMSE = 2.223%). Meanwhile, both ANN and MLR achieve optimal results with input combinations involving Tc, Ta, VPD, Rn, U, and SD (R2 = 0.992, MAE = 2.031%, RMSE = 3.705%; R2 = 0.759, MAE = 13.95%, RMSE = 19.98%, respectively). For RFR, the ideal input combination comprises Tc, Ta, VPD, and U (R2 = 0.951, MAE = 5.023%, RMSE = 9.012%). The study highlights the considerable promise of ML models in predicting CWSI, proposing their future application in integration into an irrigation decision support system (IDSS) for crop stress mitigation and efficient water management in agriculture.

Advanced Technologies for Sustainable Water Management: A Comprehensive Review

This review paper provides a comprehensive analysis of the latest advancements in water management technology, focusing on the integration of Internet of Things (IoT), machine learning, blockchain, and other emerging technologies. The study examines 19 key research papers, exploring various methodologies, key outcomes, and the challenges associated with these technologies. While significant progress has been made in improving water resource management through real-time monitoring, predictive analytics, and decentralized data management, several challenges persist, including high implementation costs, integration complexities, and the need for standardized protocols. The review identifies opportunities for future research, particularly in developing cost-effective solutions, enhancing interoperability, and advancing artificial intelligence applications in water management. Additionally, the paper highlights the importance of evolving regulatory frameworks and increasing public awareness to ensure the successful adoption of these technologies. The findings of this review contribute to the ongoing efforts to create more sustainable and efficient water management systems capable of addressing the growing global water demand.

Farmers Assistance Program Based on Artificial Intelligence (AI) and Big Data

This farmers assistance program aims to build an efficient and intelligent agricultural management system through the deep integration of artificial intelligence (AI) and big data technologies, so as to improve agricultural production efficiency, ensure the quality of agricultural products, promote sustainable agricultural development, and ultimately achieve the goals of increasing farmers' income and prosperity in rural areas.

Adaptation Strategies of Collective Management Institutions in Facing Digital Disruption in The Creative Industry

Digitalization has brought significant disruption to the creative industry, including in Indonesia. This change challenges Collective Management Organizations (CMOs) to adapt their strategies for managing copyrights and royalty distribution in the digital era. This study aims to examine the adaptation strategies implemented by CMOs in Indonesia in response to digital disruption, with a specific focus on the music sector. The research employs a qualitative method with a case study design. Data were collected through in-depth interviews, Focus Group Discussions (FGD), participatory observation, and document analysis. The findings indicate that CMOs face significant challenges in terms of operational efficiency, transparency, and fairness in royalty distribution. To address these challenges, CMOs have adopted digital technologies and developed more efficient management systems. The contribution of this study is to provide insights into how CMOs can effectively adapt to digital disruption, which could serve as a guide for future policy and strategy development.