Performance Analysis Optimization Research Articles

Application of MEMS technology in anti-electromagnetic radiation maternity clothes: state of the art and future perspectives

This review provides a comprehensive analysis of the application of Micro-Electro-Mechanical Systems (MEMS) technology in anti-electromagnetic radiation maternity wear. The review commences with an elaboration of the electromagnetic shielding principles of traditional materials and the principle of anti-electromagnetic radiation. Subsequently, the role of MEMS in maternity clothing is detailed, including the real-time monitoring of radiation via sensors, the enhancement of fabric shielding through electrospinning and material deposition, and the realization of intelligent functions such as micro-actuators and communication modules. Furthermore, the review considers the optimization of performance, taking into account factors such as electromagnetic shielding, air permeability and comfort. Furthermore, the article addresses the challenges of ensuring comfort and power supply. The article concludes by emphasizing the potential of MEMS in protecting pregnant women and fetuses and proposes future research directions, including an in-depth exploration of the working principles, technical specifications, and performance characteristics of key MEMS components (sensors and micro-actuators), as well as research on the combination and The combination of MEMS technology with existing anti-radiation technologies, such as traditional metal fiber fabrics and nanomaterials, has the potential to yield significant synergistic effects. Furthermore, an in-depth analysis of performance optimization, including durability and washing stability of maternity clothes, is essential. Additionally, the exploration of emerging technologies, such as bubble electrospinning in maternity clothes, could lead to innovative applications in this field.

Analysis of Optimal Stock Performance Using the Discounted Cash Flow Method and Stock Price Forecasting Using the Holt-Winters Method

The high liquidity of PT Perusahaan Gas Negara Tbk shares and significant fluctuations in share prices create uncertainty that requires in-depth analysis. Stock performance analysis is carried out with 2 stages in outline, namely the stages of fundamental analysis and technical analysis. Fundamental analysis is carried out to analyze optimal stock performance, one of which is by using the discounted cash flow (DCF) method. Technical analysis is used to determine the condition of stock performance in the future by forecasting stock prices using the Holt-Winters forecasting method. The objectives of this study are to analyze optimal stock performance, forecast stock prices, and evaluate forecasting results. The data used is PGN's 2023 annual report and daily data on PGN's stock price for the period January 1, 2019, to December 31, 2023, totalling 1,231 data. The results of the optimal stock performance analysis show that PGN's stock performance is declared optimal with an intrinsic value of 3,149.18, which is greater than the current stock price (undervalued). The results of stock price forecasting show that the forecasting results follow the actual data pattern, with an accuracy value using MAPE (mean absolute percentage error ) of 10.9%, it is stated that the forecasting performance has performed well.

UAV-assisted dual-hop RF communication: Performance analysis of outage probability and altitude optimization for multi-user systems

Unmanned Aerial Vehicles (UAV) are growingly used as communication relays in this modern era due to their unique capabilities and advantages which includes enhanced connectivity, cost effectiveness, flexibility, risk reduction and real time data surveillance. UAVs offer a versatile and efficient solution for enhancing communication networks, especially in situations where traditional infrastructure is inadequate or unavailable. In this manuscript, we present the dual hop UAV enabled wireless communication system where the data is broadcasted from Source (S) to Destination (D) through a UAV working as decode and forward (DF) relay (R). The data is transmitted from S to R via Radio frequency (RF) link and then the decoded data from R is forward to D with multiple users. Closed form analytical expressions of outage probability is analyzed which is further utilized to do the optimization analysis to find the optimum altitude of the UAV in order to exaggerate its system performance. Numerical results shows that there is need to find out the optimal altitude of the UAV so as to keep down the overall outage probability of the proposed system and to enhance the system quality. We also find the outage probability at higher Signal-to-Noise ratio (SNR) to study the system behavior deeply. It is clearly observable from the numerical analysis that the exact outage probability and asymptotic outage probability are exactly matching at higher SNR values concluding the validity of the proposed system. The derived expressions are authenticated by simulated results via Monte Carlo simulations.

Quantitative Analysis of TPU Microstructure and Performance Optimization across Various Processing Conditions.

Thermoplastic polyurethane (TPU) is essential in resource exploration, healthcare, automotive, and high-end recreational sports. Despite extensive research on TPU's microstructures and their macroscopic properties, the impact of processing conditions like compression and injection molding remains underexplored. This study investigates the influence of processing conditions on TPU by preparing samples with varying hard segment contents using compression molding at 205 °C and injection molding at melt temperatures of 205, 210, 215, and 220 °C, followed by heat treatment at 120 °C for 12 h. Results indicate that injection-molded TPU at 205 °C exhibits lower hydrogen bonding, crystallinity, long period, interfacial thickness, and lamella thickness than compression-molded TPU, leading to higher Young's modulus but lower elongation at break. As melt temperatures increase, these microstructural parameters decrease, reducing Young's modulus and increasing elongation at break. Post heat treatment, microstructural parameters increase, aligning Young's modulus with that of compression-molded samples, while elongation at break surpasses them. This suggests that heat treatment enhances microphase separation by rearranging hard and soft segments. our research reveals a consistent pattern across TPUs with varying hard segment contents, indicating that adjusting processing parameters can effectively regulate microstructure and performance, offering valuable insights for developing high-performance polyurethanes.

Molecular Communications Based on Probabilistic Constellation Shaping: Optimal Detection and Performance Analysis

Molecular Communications Based on Probabilistic Constellation Shaping: Optimal Detection and Performance Analysis

Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

Design optimization and off-design performance analysis of one-dimensional supercritical CO2 Brayton cycle

Supercritical carbon dioxide (SCO2) recompression Brayton cycle (RBC) is an encouraging technology for thermal power conversion and heat harvest for a wide range of heat sources due to its high efficiency, low auxiliary power consumption, and compact size. However, most studies on its design and off-design performance analysis are limited to the simple zero-dimensional component models without considering aerothermodynamics and ambient conditions. In this study, we developed a set of fully one-dimensional components models for SCO2 compressors, turbines, and heat exchangers and, then, applied them to the multi-objective design optimization of a SCO2 cycle targeting a 50 MW net power output. The cycle off-design performance analysis methods and the control strategies were developed to countermeasure the performance degradation against varying cooling water temperatures and part loads. The multi-objective cycle design optimization demonstrates that the cycle thermal efficiency and total product unit cost can be 0.476 and 11.652$/GJ, respectively, for the 50 MW SCO2 RBC cycle. The choke margin of the main compressor is 0.136 less than that of the re-compressor, and condensation can lead to a sharp decline in efficiency under high-flow conditions, causing the earlier onset of choking. The off-design performance analysis shows that the turbine inlet pressure control improves cycle efficiency below the design point of 317 K but does otherwise above the point. In contrast, the inventory control strategies can maintain stable cycle efficiency and achieve adjustable net power output between 10 % and 110 %. The findings have the potential to advance further commercial application of SCO2 cycles in high-temperature thermal energy systems.

Multi-objective optimization and off-design performance analysis on the ammonia-water cooling-power/heating-power integrated system

The utilization of ammonia-water as a working fluid in energy conversion systems presents a promising approach for efficient geothermal energy development. In this paper, a novel ammonia-water cooling-power/heating-power integrated system is presented, a structure design method suitable for the integrated thermal system is developed, and a comprehensive analysis of system performance using thermodynamic, economic, and off-design analytical methods is conducted. This study explores the impact of various variables on system design and off-design performance. The results demonstrate that optimal design performance is achieved at evaporation temperatures of 3.5 °C (cooling-power) and 20 °C (heating-power), and the hot-part temperature difference is 21 °C. Lower mass flow rate and ambient temperature lead to improved off-design performance. Due to the influence of pressure on the phase transition temperature of geothermal water, the position of the phase transition zone has shifted, resulting in a change in the mass flow rate of ammonia-water. When the pressure ratio drops below 0.9, the heat release during the phase transition of geothermal water is incomplete, leading to a significant decrease in pump speeds by 44.76 % and 41.14 % in the two modes respectively. This work provides valuable insights and references for the design and optimization of integrated thermal systems.

Optimization screening, performance and mechanism analysis of green and low-carbon surfactants for oil-based drilling cuttings treatment

Oil-Based Drilling Cuttings (OBDC) have attracted extensive attention from scholars due to their high annual generation and high toxicity. At present, the thermal cleaning technology of surfactant is one of the research hotspots for treating OBDC, but it still faces the problems of high toxicity of surfactant, high carbon emission and unknown treatment mechanism. Therefore, we sequentially optimized screening the surfactants through the perspectives of greenness, treatment performance and costs, and low-carbon. A single-variable experiment was used to optimize the process parameters for treating OBDC, and the oil removal mechanism was explored by oil droplet size, TSI, Zeta potential and SARA fractions. The SLM was the most green and low carbon surfactant in this study, and had a better treatment performance for OBDC (51.56% oil removal rate). The optimal treatment parameters for SLM treatment of OBDC were 70 °C, 45 min, 300 r/min, 1:4 (solid/liquid) and pH 8.2. The lipophilic groups of SLM "striped" the asphaltenes and saturates in oil phase from the solid phase surface and "integrated" them into the water phase by its hydrophilic group. The mean value of the oil droplet sizes (Dmean) and Turbiscan stability index (TSI) value of the OBDC-water mixtures increased and the |Zeta potential| decreased after treatment, and the mixture system became more unstable, which promotes the subsequent separation of the oil phase from the solid phase.

Optimization of opinion mining classification techniques using dragonfly algorithm

<p>With the rapid evolution and growth of the internet, many individuals are using websites, blogs, and social media, and sharing their opinions about any product or service on online social platforms. Opinion mining (OM) is a field of analyzing opinions or reviews given by the public about services or products on online resources into positive, negative, or neutral classes. Governments, businesses, and researchers are using OM to analyze the reviews or opinions of the public. Thus, OM is helping individuals and businesses in better decision making. This paper mainly focuses on the feature extraction, performance analysis of OM classifiers and optimization using swarm intelligence (SI). Our proposed work: i) evaluates the performance of OM classification techniques after data collection, pre-processing, and feature extraction, ii) applies the dragonfly algorithm (DA) for optimization, and iii) evaluates the performance of OM classification techniques after applying DA and compares it with the observed performance of OM classifiers before optimization. The experimental results show that OM classification techniques perform better after optimization using DA in terms of precision, recall, f-score, and accuracy.</p>

Enhancing PEMEC Efficiency: A synergistic approach using CFD analysis and Machine learning for performance optimization

The proton exchange membrane electrolytic cell (PEMEC) is a clean, pollution-free, and promising device for producing hydrogen from water electrolysis. This paper enhances the electrochemical performance of the PEMEC by developing a comprehensive three-dimensional two-phase non-isothermal heat and mass transfer cell model. An analysis was conducted to study the influence of various operating parameters on the temperature safety and efficiency of the cell. The performance of the latter was optimized by adopting the machine learning technology, which aimed at identifying optimal conditions for improved performance. The obtained results showed that, when the inlet water flow rate and membrane thickness were increased, the average temperature of the anode catalyst layer (A-CL) was decreased by 1.4 K and 2.31 K, respectively. This allowed to improve the safety of the electrolytic cell. However, it would reduce the hydrogen and oxygen mole fraction in the electrolyzer. On the contrary, enhancing the porosity or thickness of the anode gas diffusion layer can increase the average A-CL temperature and decrease the oxygen and hydrogen mole fraction. Therefore, the backpropagation neural network and non-dominated sorting genetic algorithm were used to predict and optimize the performance of the electrolyzer, in order to maximize its temperature safety, electrochemical reaction rate, and hydrogen evolution efficiency as much as possible. The results of this study provide a theoretical foundation for the selection of suitable cell models according to different conditions in engineering applications. It also has an application value in energy saving and sustainable development.

Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis

Portfolio management (PM) is a popular financial process that concerns the occasional reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of maximizing profitability conditioned to a certain level of risk. Given the inherent dynamicity of stock exchanges and development for long-term performance, reinforcement learning (RL) has become a dominating solution for solving the problem of portfolio management in an automated and efficient manner. Nevertheless, the present RL-based PM methods just take into account the variations in prices of portfolio assets and the implications of price variations, while overlooking the significant relationships among different assets in the market, which are extremely valuable for managerial decisions. To close this gap, this paper introduces a novel deep model that combines two subnetworks; one to learn a temporal representation of historical prices using a refined temporal learner, while the other learns the relationships between different stocks in the market using a relation graph learner (RGL). Then, the above learners are integrated into the curriculum RL scheme for formulating the PM as a curriculum Markov Decision Process, in which an adaptive curriculum policy is presented to enable the agent to adaptively minimize risk value and maximize cumulative return. Proof-of-concept experiments are performed on data from three public stock indices (namely S&P500, NYSE, and NASDAQ), and the results demonstrate the efficiency of the proposed framework in improving the portfolio management performance over the competing RL solutions.

Optimal Design and Performance Analysis of Multiple Photovoltaic with Grid-Connected Commercial Load

Optimal Design and Performance Analysis of Multiple Photovoltaic with Grid-Connected Commercial Load

Optimal Design and Performance Analysis of a Gas-Gas Heat Exchanger for Harvesting Waste Heat

This study reports the design of an optimized heat exchanger capable of efficiently extracting and transferring waste heat from flue gas to air. An experimental setup was constructed to recover waste heat from the exhaust steam of a boiler, using this heat to warm compressed air. Thermocouples recorded the temperatures at the inlet and outlet of both the hot and cold fluids. A MATLAB code is developed to compare the experimental results. According to our code, the cold fluid outlet temperature ranged from 71.5°C to 76°C, while the experimental setup showed temperatures ranging from 66°C to 71.5°C at air mass flow rates of 0.1564, 0.1346, and 0.1794 kg/s. The deviation may arise from the inaccuracies of the flow rate and the temperature measurement devices as well as the constraints in correlations which calculate heat transfer coefficient and friction factor in pressure drop. Future work may focus on minimizing the deviations and propose an improved design of heat exchanger. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 124-141

Optimization and working performance analysis of liquid cooling plates in refrigerant direct cooling power battery systems

Refrigerant direct cooling is currently being considered as an efficient thermal management technology in power battery systems. In this paper, four types of liquid cooling plates for power battery modules are designed and the computational model is constructed. With the model being validated, it is applied to analyze the effects of the cooling plate structure and cooling channel on the cooling and heat dissipation performances. The results reveal that for the conventional cooling method, i.e., when the cooling plate is placed on the bottom of the battery pack, a significant temperature gradient in the vertical direction is observed. On the contrary, adopting a serpentine cooling plate structure in the main wall or parallel cooling channels in the narrow side wall could significantly reduce the temperature difference of the battery pack, which can keep the temperature difference within 5 °C even under extreme conditions. Further, a detailed analysis of the heat dissipation performance based on the optimal cooling plate structure is performed to determine the safe operating range of the battery pack. It is shown that as the humidity is less than 0.73 and the evaporation temperature is below 15.43 °C, the battery pack can operate safely. However, beyond this condition range the heat dissipation characteristics of the battery pack cannot satisfy the operating requirements. This work sheds light upon the potential of refrigerant direct cooling strategy in power battery thermal management systems by properly arranging the cooling plate.

Performance Optimization and Techno-Economic Analysis of an Organic Rankine Cycle Powered by Solar Energy

Abstract To improve the performance of traditional solar power generation systems, a new solar organic Rankine cycle system that can generate electricity and heat is proposed. The system incorporates the separation-flash process, regenerator, and ejector to enhance its efficiency. The optimization of the working fluid, pinch point temperature difference, evaporator outlet dryness, flash dryness, and entrainment ratio is conducted to achieve optimal performance. Aiming at maximum exergy efficiency and minimum levelized energy cost, the operating parameters are further optimized using a multi-objective optimization algorithm. R245fa is the optimal working fluid for the system, offering maximum net output power and thermal efficiency. The optimal performance can be achieved when the pinch point temperature difference is 1 K, evaporator outlet dryness is 0.6, flash dryness is 0.44, and entrainment ratio is 0.29. Moreover, the photovoltaic subsystem can further increase the net output power and thermal efficiency by 15.52% and 15.45%, achieving a maximum net output power and thermal efficiency of 33.95 kW and 10.61%. Additionally, when the solar hot water temperature is 100 °C, pinch point temperature difference is 1.8 K, evaporator outlet dryness is 0.6, flash dryness is 0.65, and entrainment ratio is 0.16, the system can achieve the optimal state of both performance and economy, exhibiting an optimal exergy efficiency and levelized energy cost of 64.1% and 0.294 $/kWh, respectively. Finally, the payback period of system is 3.43 years, indicating the potential for significant economic benefits.

An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Renewable Energy Distributed Energy System Optimal Configuration and Performance Analysis: Improved Zebra Optimization Algorithm

The use of distributed energy systems (DES) can utilize local resources to achieve flexible and efficient energy production and supply. However, this aspect of pollutant emission reduction has not been sufficiently investigated in current related studies. On this basis, this study establishes a DES system that integrates a ground-source heat pump, a gas turbine, a photovoltaic device and an energy storage device. An Improved Zebra Optimization Algorithm (IZOA) is proposed for optimizing the capacity of DES devices and the energy supply ratio of the ground-source heat pump. Using the economic cost saving rate (ECSR), pollutant emission reduction rate (PERR) and energy saving rate (ESR) as the optimization objectives, the study builds a DES configuration optimization model. By analyzing the arithmetic example of a large hotel building, the study verifies the effectiveness of the IZOA algorithm in solving the DES configuration optimization problem. This study provides useful research ideas in promoting the development of distributed energy systems, environmental protection and energy conservation.

A large-scale multivariate soccer athlete health, performance, and position monitoring dataset

Data analysis for athletic performance optimization and injury prevention is of tremendous interest to sports teams and the scientific community. However, sports data are often sparse and hard to obtain due to legal restrictions, unwillingness to share, and lack of personnel resources to be assigned to the tedious process of data curation. These constraints make it difficult to develop automated systems for analysis, which require large datasets for learning. We therefore present SoccerMon, the largest soccer athlete dataset available today containing both subjective and objective metrics, collected from two different elite women’s soccer teams over two years. Our dataset contains 33,849 subjective reports and 10,075 objective reports, the latter including over six billion GPS position measurements. SoccerMon can not only play a valuable role in developing better analysis and prediction systems for soccer, but also inspire similar data collection activities in other domains which can benefit from subjective athlete reports, GPS position information, and/or time-series data in general.

Performance optimization and techno-economic analysis of a novel geothermal system

A novel geothermal system that has high efficiency and economic benefits was proposed in this study. The proposed geothermal system includes an organic Rankine cycle, absorption refrigeration subsystem, and heating/hot water subsystem. The selection of working fluid, performance optimization, and techno-economic analysis of the geothermal system were conducted by establishing mathematical models. The thermodynamic performance was enhanced by using evaporators in series and the separation-flash process. And the thermodynamic performance of the proposed system was investigated with five working fluids. The ratio of mass flow rate, separate pressure, and flash pressure were optimized using the stepwise multi-objective optimization technology. The results show that R245fa was the optimal working fluid. The system can reach its optimal operating state when the ratio of mass flow rate is 2.5, separate pressure is 2500 kPa and flash pressure is 1000 kPa. The largest power generation, thermal efficiency, exergy efficiency, and the lowest irreversible loss of the optimized system are 962.5 kW, 13.64 %, 35.74 %, and 4248 kW, respectively. The techno-economic analysis shows that the total investment cost of the novel geothermal system is 8.173–9.132 million dollars, the annual revenue is 2.451–2.608 million dollars per year, and the static payback period is 3.134–3.726 years.