Optimal System Efficiency Research Articles

Optimizing System Efficiency and Reliability: Integrating Semi-Markov Processes and Regenerative Point Techniques for Maintenance Strategies in Plate Manufacturing

This paper offers a modeling methodology to evaluate similar systems’ performance and optimize system effectiveness with customized maintenance plans. This strategy, which combines regeneration point techniques with semi-Markov processes, is used to assess a plate manufacturing company’s system. Key insights are obtained using a thorough cost-benefit analysis that includes numerical analysis, graphical interpretations, and system efficacy indicators. The study clarifies the dynamics of the system’s reliability under different repair rates by showing an inverse relationship between Mean Time to System Failures (MTSF) and failure rate. Additionally, the analysis clarifies how profit is affected by failure rate, highlighting the necessity of efficient maintenance plans from an economic standpoint. This research highlights the growing significance of availability and dependability in technology-driven sectors and emphasizes the need for dependable systems. This paper makes a substantial contribution to the knowledge already available in the field of reliability engineering by providing a thorough framework that combines regeneration point approaches and semi-Markov processes. The metrics that are obtained offer a comprehensive understanding of the behavior of the system, which in turn helps industry practitioners make well-informed decisions.

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals.

Highly efficient spectrum-splitting solar energy utilization based on near- and far-field thermophotovoltaics

Improving the efficiency of solar energy utilization systems is of great importance in solar energy utilization. Near-field thermophotovoltaic cells can achieve a high power density and, therefore, efficiently utilize radiative heat transfer. An efficient spectrum-splitting solar energy utilization system based on near- and far-field thermophotovoltaics was proposed. The input solar energy was divided by the splitter, and the two parts of the energy were utilized differently. The concentration ratio can determine the energy input of the system, which can affect the photoelectric conversion efficiency of the system. Further, the vacuum distance can modulate the near-field radiative heat transfer, thus significantly impacting system performance. Based on the fluctuation dissipation theorem and Maxwell's equations, a system model was constructed and calculated using the optimal design method, and the effects of these parameters were investigated. Moreover, the optimal efficiency was obtained under different operating conditions. The optimal cascade system efficiency reached 39.93 % at a concentration ratio of 3000, which was 15.59 % higher than that of a single photovoltaic system. Thus, this study proposes a method for the efficient utilization of solar energy. In addition, it provides guidance for the subsequent design and construction of spectrum-splitting cascade systems based on near- and far-field thermophotovoltaics.

Comparison of entransy loss analysis and the first law of thermodynamics in the optimization of organic Rankine cycle coupled with parabolic trough collector

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss (Ġloss) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, (Ġloss) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the Ġloss). In addition, the optimization parameters included turbine inlet temperature (Ttur), boiler pressure (Pboil), condenser pressure (Pcond), and the temperature of the collector fluid at the boiler outlet (Thf,out). This optimization was performed for the temperature of the collector fluid at the boiler inlet (Thf,in) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and Thf,in, scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1.

Value-Based Radiology in Canada: Reducing Low-Value Care and Improving System Efficiency.

Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of "low-value" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.

Design and optimization of space nuclear power system with sCO2 Brayton cycle on Mars

As human exploration of outer space deepens, the demand for space power increases. This requires not only higher thermoelectric conversion efficiency but also a more compact system. Space nuclear power based on the sCO2 Brayton cycle is better suited for the surface of Mars due to its high efficiency and low ratio of mass to electric power. This paper designs a 6 MWt sCO2 Brayton cycle power generation system, and the following system configurations are mainly studied including Simple Heat Recovery Cycle (SRC), Re-Compress Heat Recovery Cycle (RC), Reheating Heat Recovery Cycle (RRC), and Reheating Re-Compress Cycle (RHRC). A thermodynamic calculation model of the system was developed based on the aforementioned cycle configurations. Additionally, a quality assessment model was developed separately for each component within the system, according to its respective category. The effects of parameters such as temperature, compressor pressure ratio, and bypass ratio on the system efficiency and total system mass are also examined for each of the four-cycle configurations. The study conducted a global multiparameter optimization using a genetic algorithm to optimize system efficiency and the ratio of mass to electric power. The RHRC configuration achieved the highest cycle efficiency at 39.47%, while the SRC cycle had the lowest system efficiency at 35.46%. The RRC configuration had the minimum ratio of mass to electric power at 7.419 kg/kWe with an efficiency of 33.13%.

Efficiency optimization of fuel cell systems with energy recovery: An integrated approach based on modeling, machine learning, and genetic algorithm

Maximizing the efficiency of fuel cell systems (FCS) with energy recovery capabilities is crucial for advancing high-efficiency FCS technology. This research aims to explore the maximization of FCS efficiency through the optimization of operational parameters and to elucidate the synergistic effects between parameter optimization and energy recovery. Employing an integrated approach that combines model simulation, experiments, machine learning, and genetic algorithm (GA), this work addresses key technical challenges in developing an efficient FCS model and a high-precision surrogate model. It also tackles the critical problem of achieving maximum system efficiency through the optimization of operational parameters and energy recovery. The findings indicate that the data-injection-based model simplifies the computational process and effectively validated over long time scales. Feature selection and network quality assessment ensure the precision and reliability of the long short-term memory (LSTM) network. Optimization using GA in conjunction with the LSTM surrogate model enables a rapid and accurate determination of the system's maximum net output power and corresponding operational conditions. The results demonstrate a 6 % increase in the system's net output power, with the system efficiency consistently surpassing 55 %. Moreover, energy recovery consistently boosts optimized system efficiency by about 1.6–1.7 %.

Enhancing Decision Fusion for Wastewater Treatment System Selection Using Monte Carlo Simulation and Gray Analytic Hierarchy Process

This research presents an innovative data fusion model that utilizes Monte Carlo simulations (MC) and the Gray Analytic Hierarchy Process (G-AHP) to address the complexity and uncertainty in decision-making processes, particularly in selecting sustainable wastewater treatment systems. The study critiques and extends the Dempster–Shafer and Yager’s theories by incorporating a novel MC algorithm that mitigates the computational challenges of large numbers of experts and sensors. The model demonstrates superior performance in synthesizing diverse expert opinions and evidence, ensuring comprehensive and probabilistically informed decision-making under uncertainty. The results show that the combined MC algorithm produces satisfactory results, and thus, offers wide applicability in decision-making contexts. To determine its effectiveness, an extensive empirical study was conducted to identify an appropriate wastewater treatment system for the busy city of Tehran, incorporating the insights and perspectives of respected experts in the field. The selection was based on three technical, economic, and environmental–social criteria. Due to the large dimensions of each of the defined criteria, sub-criteria were also defined to achieve better results for each of the criteria. The in-depth analysis conducted revealed that enhanced aeration activated sludge (EAAS) emerged as the best choice for Tehran’s most urgent needs among various competitors, with a remarkable priority rating of 34.48%. Next, the Gray Analytic Hierarchy Process (G-AHP) was used to determine the most important sub-criterion, based on which resistance to hydraulic shock is most important in the enhanced aeration activated sludge system. Due to its versatility in different fields and industries, this method is a powerful tool for managers to optimize system efficiency and identify defects and risks and eventually to minimize costs.

Working mode division and shift schedule extraction of a parallel hybrid power system

Abstract For hybrid electric vehicles, working mode division and shift schedule are two decisive factors affecting vehicle fuel economy. This paper introduces a method for vehicle working mode division and shift schedule determination on the basis of optimal system efficiency. The key index named ‘fuel-electricity conversion efficiency’ is defined and calculated. Via this method, unreasonable gear modes in specific working modes are excluded and the mode transition map and upshifting curves in each working mode are extracted. The simulation results demonstrate that, with the application of the single-motor multi-mode parallel hybrid power system, the proposed mode transition and shifting strategy can increase vehicle fuel economy by 12.99%.

Dual-Axis Solar Tracker

Solar energy is becoming a popular technique to develop sustainable energy sources. Engineering professionals must understand the technologies involved. Solar panel tracking system design and construction are covered in this project. Solar tracking orients the solar array toward the sun, increasing energy output. This system expands on course topics. Solar energy feasibility requires solar array system efficiency optimization. Solar array systems can be improved by sun tracking. This method keeps a solar array facing the sun at all times. Solar modules cleanly turn sunshine into energy in remote areas, solving the power generating problem. The solar tracker designed for this project is reliable and affordable for aligning a solar module with the sun to maximize energy output. Hardware/software hybrid prototype Automatic Sun Tracking System improves solar panel alignment with the sun to maximize energy production and inspire design. Problems and solutions will be discussed.

Enhancing Closed System Efficiency through CuO Nanofluids: Investigating Thermophysical Properties and Heat Transfer Performance

Working fluids play a crucial role in closed systems to ensure efficient performance, particularly in systems for heating, cooling, or power generation, where the heat transfer coefficient is pivotal. This study delves into the thermodynamic properties and stability of copper oxide (CuO) nanofluids as alternative working fluids in closed systems. Investigating colloidal suspensions of CuO nanoparticles, the research aims to enhance heat transfer efficiency. CuO nanoparticles, sized at 40nm and 80nm, were dispersed in base fluids like water, ethylene glycol, and oil sans surfactants. The study, divided into static and dynamic phases, examines key nanofluid properties including viscosity, thermal conductivity, specific heat, and heat transfer rate. Through methodologies such as KD2 Pro for thermal conductivity, rheometer for viscosity, and small heat exchanger for heat transfer rate analysis, the effects of volume concentration, temperature, and nanoparticle size on nanofluid performance were evaluated. Sedimentation analysis employed both quantitative (standard deviation calculations) and qualitative (sediment capture methods) approaches. The findings highlight the superior heat transfer rate of 40nm CuO nanofluid at 0.467% volume concentration which is 9.08 kJ/s, suggesting its potential to optimize system efficiency, particularly in heating, cooling, and power generation applications.

P-702 Centrifugal Pump Damage Analysis Insights and Implications

Centrifugal pumps are widely used in industries for fluid transfer, operating on the rotation of an impeller driven by a prime mover. However, sudden performance drops and operational instability pose significant challenges, impacting overall system efficiency. Predictive maintenance relies on vibration analysis to assess rotating equipment condition, with vibration patterns indicating potential issues such as cavitation and corrosion. Prolonged cavitation leads to erosion of channel wall surfaces, resulting in structural damage. This study aims to address the knowledge gap in understanding the relationship between pump vibration, cavitation, and corrosion. Through comprehensive vibration analysis and examination of pump performance, our research identifies the mechanisms driving pump degradation. The findings highlight the critical role of predictive maintenance in mitigating pump failures and optimizing system performance. Highlights : Vibration analysis crucial for predictive maintenance: Identifying early signs of pump deterioration. Cavitation-induced erosion: Understanding the impact of prolonged cavitation on pump performance. Importance of proactive maintenance: Mitigating pump failures and optimizing system efficiency. Keywords : Centrifugal pumps, Vibration analysis, Cavitation, Corrosion, Predictive maintenance

Development of a Volkswagen Jetta MK5 Hybrid Vehicle for Optimized System Efficiency Based on a Genetic Algorithm

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model’s accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved.

Optimization of Radio Energy Transmission System Efficiency Based on Genetic Algorithm

In order to better understand the efficiency optimization problem of radio energy transmission systems, the author proposes a genetic algorithm based research on efficiency optimization of radio energy transmission systems. The author first addresses the issue of improving the efficiency of magnetic coupled radio energy transmission. On the basis of ensuring a certain transmission distance and voltage gain, the system is mathematically modeled using coupling circuit theory, and mathematical expressions such as transmission efficiency, transmission distance, and voltage gain are obtained as the objective functions of the algorithm. Secondly, the impact of metal obstacles on the transmission system was analyzed. Design a radio energy transmission compensation circuit, and through simulation, obtain three transmission system parameter schemes that meet the objective function and constraint conditions. Finally, the multi-objective genetic algorithm is used to optimize the system parameter design and obtain the optimal combination of transmission system parameters, with coupling coefficient k=0.1818 and mutual inductance coefficient M=23.165 × 10^(-5) H. Using multi-objective genetic algorithm, the algorithm has a fast convergence function in terms of the number of iterations, a non dominated function solution, and Pareto graphs have verified that the numerical value (3) in the text is the optimal combination design for the transmission system.

Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements.

Improving efficiency and sustainability of water-agriculture-energy nexus in a transboundary river basin under climate change: A double-sided stochastic factional optimization method

In this study, a double-sided stochastic fractional programming (DSFP) method is developed for identifying optimal water-allocation schemes of water-agriculture-energy nexus (WAEN) system under considering climate change impact. The advantages of DSFP are (i) handling complex uncertainty of double-sided randomness, (ii) addressing conflicting objectives with optimal system efficiency, and (iii) achieving trade-off between marginal benefit and system risk. Then, a DSFP-based water-agriculture-energy nexus (DSFP-WAEN) model is formulated for water resources allocation in a transboundary river basin of Central Asia, where 48 scenarios are designed to examine the impacts of climate change, irrigation efficiency, and system risk over a long-term planning horizon (2026–2050). Results reveal that: (i) among all agricultural activities, cash crop cultivation shows the greatest change in adaption to climate change, with the share of cash crop cultivation increasing to 70.7% by 2050 under RCP4.5; (ii) Kazakhstan, which has the largest irrigation needs and is the county most sensitive to water supply risk, should be the first to be constrained in the case of severe water shortages and poor delivery levels; (iii) the improvement of irrigation efficiency can increase the inflow to the Aral Sea by 2.7%, and reduce water loss of infield irrigation by 11.3% even under severe water shortage (i.e. p = 0.01). Compared with the models based on the traditional stochastic programming and single-objective methods, DSFP-WAEN has advantages in optimizing water-use efficiency and reflecting system complexity with flexible solutions. To coordinate transboundary water conflicts, strategies from both demand and supply sides related to quota management and water-saving techniques are suggested for the Syr Darya River basin, such as clean energy transition, drip irrigation promotion and canal system improvement. From a long-term planning perspective, WAEN schemes should be adapted to risk attitude and climate change, which can help address water scarcity and achieve future sustainability.

Efficiency Optimization in Core Electrical Systems through Smart Energy Solutions

Efficiency Optimization in Core Electrical Systems through Smart Energy Solutions

Comparison of Energy Storage Management Techniques for a Grid-Connected PV- and Battery-Supplied Residential System

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer’s operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS.

A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. : Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. Methods: The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. Results: The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. Conclusion: The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.