System Performance Parameters Research Articles

Predicting the Ammonia Synthesis Performance of Plasma Catalysis Using an Artificial Neural Network Model

Ammonia synthesis in a plasma catalysis system coupling dielectric barrier discharge and an alumina-loaded ruthenium catalyst was investigated. The discharge energy, the N2 to H2 ratio, and the reactant flow rate greatly affect the production of NH3 and the efficiency of energy. Higher ammonia synthesis rates and higher energy yields were obtained at high discharge powers and flow rates. An artificial neural network (ANN) was built to describe the influence of operating parameters on the NH3 synthesis performance, including ammonia synthesis rate and energy yield. The proposed ANN model was trained using experimental data. The results showed the N2 to H2 ratio was the most impactful parameter with a relative importance of 41.7% on the model, followed by the flow rate and discharge power of 32 and 26.3%, respectively. This ANN model can effectively help to optimize the operating parameters of the plasma catalysis system for NH3 synthesis and predict the catalysis performance under specific situations.

Predictive investigation of the combined heat storage system using at NPPs with VVER

The increase in the share of nuclear power plants in the energy systems leads to the need for their participation in covering the irregularities in the electrical load schedules. NPPs unloading is economically unsustainable. Combining NPPs with storage systems can be a solution to this problem, as it will allow the reactors to operate at a constant capacity utilization factor, while excess energy will be stored during load dip hours and converted into electrical energy during peak load hours. The authors have developed a thermal storage system based on the combination of a phase change accumulator with hot and cold water tanks. The system also contains a steam turbine to generate electricity during peak hours using stored energy. Earlier the authors proved that a low-power turbine can be used to supply power to the NPP auxiliary needs with a blackout using only residual heat energy. In this paper, a methodology for determining the necessary operating parameters of the storage system for using under the required conditions is presented. The investigation of economic efficiency was carried out on the example of a range of electricity tariffs for the world’s energy systems. Accumulated net present value ranged from $0 to $85 million.

A novel two-stage compression air-source heat pump cycle combining space heating, cooling, and domestic hot water production

To improve the efficiency of HVAC system, a novel air-source heat pump based on the two-stage compression cycle is proposed. The heat pump shows the multi-function of producing the domestic hot water year-round, space cooling water in summer, and space heating water in winter. The theoretical model is developed to analyze the performance of heat pump under various conditions. Four high GWP refrigerants, including R134a, R32, R407, R410A, and four low GWP refrigerants, including R1234ze(E), R600a, R152a, R290, are compared and analyzed under various conditions. Besides, the effects of environment and system operation parameters, such as domestic hot water demand ratio, evaporating and condensing temperatures, on system performance are investigated. The major results are concluded: A multi-functional air-source heat pump shows high annual efficiency by switching both the four-way valve and tee valve. The low GWP refrigerant R152a shows the best performance among the selected refrigerants, especially for summer conditions, and the COP is higher than the R1234ze(E), R134a, R600a, R32, R407C, R410a, and R290, R152a by 10.4 to 9.0 %, 8.3 to 7.9 %, 5.6 to 4.0 %, 9.1 to 13.0 %, 20.9 to 23.3 %, 29.8 to 36.5 %, and 11.0 % under the selected condition, respectively. Compared with summer conditions, R152a improves COP slightly under winter conditions, especially for R600a and R134a (less than 5 % under the selected conditions). Besides, under the summer conditions, the system performance increases first and then decreases with the increasing domestic hot water demand. The maximum COP reaches 6.7 while the summer domestic hot water load ratio (SLR) equals 0.5 and the environment air temperature equals 25 °C. The heat pump also shows a good performance under winter conditions. The minimum COP reaches 2.4 with the air temperature of −25 °C and the winter domestic hot water load ratio (WLR) of unity. The result of this study hopes to promote the further improvement of the air-source heat pump and HVAC system.

A Combined Power and Ejector Refrigeration Cycle System for Thermal Energy Recovery from the Waste Heat of Internal Combustion Engine

In this paper, due to the importance of energy recovery from internal combustion engines and the increasing human growing need for power and refrigeration, a two-cycle power and refrigeration system consisting of an organic Rankine cycle and an ejector refrigeration cycle is investigated analytically. The proposed system with the ability to use the ejector<span dir="RTL"> as an effective and economical system, can produce simultaneous power and refrigeration by drawing thermal energy from the exhaust gas and engine coolant in the form of a novel cycle. This study analyzes the proposed novel two-cycle Rankine-ejector refrigeration system (TCReRS) compared to a two-cycle Rankine-absorption refrigeration system (TCRARS). It was concluded that the TCReRS with higher output and a COP of about 69.99%, is a useful and promising system under the considered condition. The proposed system uses environmentally friendly R1233zd and R32 fluids as operating fluids in Rankine power and ejector refrigeration cycles, respectively. In addition, the effect of system performance parameters has been investigated and optimized.

Multicriteria Design and Operation Optimization of a Solar-Assisted Geothermal Heat Pump System

This work focuses on the determination of the design and operation parameters of a thermal system depending on the optimization objective set. Its main objective and contribution concern the proposal of a generalized methodological structure involving multiobjective optimization techniques aimed at providing a solution to a practical problem, such as the design and dimensioning of a solar thermal system. The analysis is based on system operation data provided by a dynamic simulation model, leading to the development of multiple surrogate models of the thermal system. The thermal system surrogate models correlate the desired optimization objectives with thermal system design and operation parameters while additional surrogate models of the Pareto frontiers are generated. The implementation of the methodology is demonstrated through the optimal design and operation parameter dimensioning of a solar-assisted geothermal heat pump that provides domestic hot water loads of an office building. Essentially, energy consumption is optimized for a desired domestic hot water thermal load coverage. Implementation of reverse-engineering methods allows the determination of the system parameters representing the optimized criteria.

Statistical Method of Low Frequency Oscillations Analysis in Power Systems Based on Phasor Measurements

This study aims to develop and test a new accelerated method for analyzing low-frequency oscillations in power systems using phasor measurements. The proposed method is based on the use of mathematical statistics methods that do not require significant computing power and have high reliability. Changes in the structure of power generation and integration of control devices based on power electronics cause low-frequency oscillations of power system operation parameters that present a threat. These changes result in a reduction in the total inertia of power systems with the subsequent impact on the operation of automatic voltage regulators and power system stabilizers, the purpose of which is to damp low-frequency oscillations. We conduct a careful review of the existing methods for low-frequency oscillations analysis in power systems to identify the gaps in the literature and design a new method to address the issues. The proposed method is tested on real-life data that was obtained during a disturbance with a transient event. Estimation of the low-frequency oscillation parameters was carried out, and the potential threat posed by these phenomena was examined. The implementation of the proposed algorithm for analyzing low-frequency oscillations is done using the Matlab programming language. Evaluation of the proposed algorithm is performed on physical data obtained during real transient processes occurring at large power plants.

Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

The current industrial scenario has witnessed the application of several artificial intelligence-based technologies for mining and processing IoMT-based big data. An emerging distributed machine learning paradigm, Federated Learning (FL), has been widely applied in IoMT-based systems as a measure to overcome the issues associated with incorporating AI into such lightweight distributed computing systems while tackling privacy issues as well. However, extensive research has identified that classical FL is still prone to privacy threats due to data leakage and the chances of adversarial attacks during gradient transfer operations. Inspired by these issues, we propose a privacy-preserving framework (Fed_select) that ensures user anonymity in IoMT-based environments for big data analysis under the FL scheme. Fed_Select utilizes alternative minimization to limit gradients and participants in system training to decrease points of system vulnerability. The framework works on an edge computing-based architecture which ensures user anonymity via the employment of hybrid encryption techniques along with added benefits of load reduction at the central server. Also, a Laplacian noise-based differential privacy is employed on the shared attributes for security enhancement that adds confidentiality to the transferred data even during adversarial scenarios. Experimental results on standard datasets showcase that the change in the volume of gradients shared and the number of participants is not proportional to the variation in various system performance parameters. Specifically, an idealistic range of client and gradient-sharing fractions along with the appropriate value of noise for differential privacy implementation is determined. Additionally, we analyze the system from a security perspective as well as compare it with other schemes.

5G MILLIMETER WAVE MIMO DIELECTRIC RESONATOR ANTENNA

A four-element rectangular dielectric resonator antenna (DRA) array for fifth generation (5G) multiple-input-multiple-output (MIMO) application at 28 GHz is presented. Each DRA element of the array has a very small dimension of 3 × 0.03 × 6 mm and oriented at an angle of 90 deg to each other. Total dimension of the proposed antenna system is 20 × 20 × 7 mm. Each antenna of the system has high radiation efficiency (< -0.004 dB). Defected ground structure is used to obtain very good isolation (> 27 dB) between any two DRA elements of the array. Moderate directivity (> 4.1 dBi) of the MIMO antenna can be increased up to 7.8 dBi using thin metal walls to separate each DRA element of the system. This MIMO antenna system is capable of giving 4 GHz impedance band width extending from 25.78 to 29.78 GHz. Computer simulation technology is used to design and investigate the performance parameters of this MIMO antenna system. Performance parameters of the proposed antenna are compared with similar types of antennas found in recent literature.

Assessment of system effects in the operation of an integrated heating and cooling systems in North

The work examines the current issues of development, management and optimization of integrated systems in the world and in Russia. The developed algorithm for evaluating the systemic effects of an integrated heating and cooling system in the conditions of the North is described. A qualitative and quantitative assessment of socio-economic, operational, and environmental effects is obtained using the example of the city of Yakutsk. The work carried out a technical and economic calculation, technical parameters of the energy system operation and volumes of CO2 emissions reduction. The use of waste heat by absorption chillers in the production of cold can reduce electricity costs by up to 29% per month of total consumption and save up to 519 million rubles per year. The implementation of the integrated heating and cooling system technology can reduce CO2 emissions by up to 58 tons.

Fault location in a marine low speed two stroke diesel engine using the characteristic curves method

<abstract><p>When a malfunction occurs in a marine main engine system, the impact of the anomaly will propagate through the system, affecting the performance of all relevant components in the system. The phenomenon of fault propagation in the system caused by induced factors can interfere with fault localization, making the latter a difficult task to solve. This paper aims at showing how the "characteristic curves method" is able to properly locate malfunctions also when more malfunctions appear simultaneously. To this end, starting from the working principle of each component of a real marine diesel engine system, comprehensive and reasonable thermal performance parameters are chosen to describe their characteristic curves and include them in a one-dimensional thermodynamic model. In particular, the model of a low-speed two stroke MAN 6S50 MC-C8.1 diesel engine is built using the AVL Boost software and obtaining errors lower than 5% between simulated values and test bench data. The behavior of the engine is simulated considering eight multi-fault concomitant phenomena. On this basis, the fault diagnosis method proposed in this paper is verified. The results show that this diagnosis method can effectively isolate the fault propagation phenomenon in the system and quantify the additional irreversibility caused by the Induced factors. The fault diagnosis index proposed in this paper can quickly locate the abnormal components.</p></abstract>

Simulasi Termodinamika Pengaruh Temperatur Subcooling di Kondensor Terhadap Kinerja Cold Storage

The subcooling temperature in the condenser can affect the overall performance of the refrigeration system. One of the refrigeration systems that plays an important role in the cold chain of fishery products is cold storage. This paper discusses thermodynamic study of the effect of condenser subcooling temperatures on the performance of cold storage that operates based on a vapor compression refrigeration cycle based on the CoolPack software. The subcooling temperatures varied was 5 – 10 Kelvin and the system performance parameters analyzed were refrigerant mass flow rate, heat rejected in the condenser, compressor power and coefficient of performance. The results of this study indicate that as the subcooling temperature increases, the refrigerant mass flow rate decreases so that the compressor power and the rate of heat release in the condenser also decrease. Thus, the performance coefficient of cold storage will increase as the subcooling temperature increases.

Impact of rotor geometry optimization on the off-design ORC turbine performance

The paper describes the method of CFD based Nelder-Mead optimization of a 10 kW single-stage axial turbine operating in an ORC system working on R7100. The total-to-static isentropic efficiency is defined as an objective function.Multi-point linear regression is carried out to determine the significance of the objective function arguments and to pick up the set of particular variables and characteristic quantities (e.g. flow angles) which contribute most to improving the objective function value. This approach enables us to considerably reduce the number of optimized parameters and computational time needed for the optimization process.Variable operating parameters of ORC power systems become an everyday occurrence, which makes the analysis of ORC turbines under variable operating parameters a critical part of the design. Therefore, the paper presents the influence of a single-criterion optimization of the rotor geometry on the performance of the ORC turbine in a wide range of operating conditions. The results of optimization indicate that new geometry provides an approximately 5.5 pp. (percentage point) increase in total-to-static efficiency for the design point, and even larger improvements reaching 7 pp. For higher turbine loads, whereas for lower loads the efficiency improvements are at the level of 1.5–4 pp.

Spectral Efficiency Analysis of Cell-Free Distributed Massive MIMO Systems With Imperfect Covariance Matrix

In this paper, the impacts of imperfect channel covariance matrix on the spectral efficiency (SE) of cell-free distributed massive multiple-input multiple-output (MIMO) systems are analyzed. We propose to estimate the channel covariance matrix by alternately using the assigned pilots and their phase-shifted pilots in different coherent blocks, which improves the accuracy of channel estimation with imperfect covariance matrix and reduces pilot overhead. Under this scheme, the closed-form expressions of SE with maximum ratio combination (MRC) and zero-forcing (ZF) receivers are derived, which enables us to select key parameters for better system performance. Simulation results verify the effectiveness of the proposed channel estimation method and the accuracy of the derived closed-form expressions. When more coherent blocks are used to estimate the covariance matrix, we can get better system performance. Moreover, some insightful conclusions are arrived at from the SE comparisons between different receiving schemes (ZF and MRC) and different pilot allocation schemes (orthogonal pilot and pilot reuse).

Multi-objective balanced method of optimizing the heat extraction performance for hot dry rock

Geothermal energy, a kind of clean and environmentally friendly energy source, is an important object of future natural resource development and utilization, among which, hot dry rock is one of the important deep geothermal resources. In the current multi-objective optimization of heat extraction performance, reservoir production models are less considered and the effects of different optimization ideas are not compared comprehensively. To improve the heat extraction efficiency and prolong the exploitation life of geothermal reservoirs, this paper determines the appropriate operating parameters of geothermal system (injection temperature, injection rate, production pressure and injection-production well spacing) based on the coupled thermal-hydraulic-mechanical model of hot dry rock exploitation in the Gonghe area of Qinghai and three heat extraction optimization methods. In addition, the heat extraction performances of different schemes are comparatively evaluated. And the following research results are obtained. First, the sensitivity analysis of injection and production parameters shows that power generation and recovery factor are in a reverse relation with injection-production pressure difference, which is the direct reason for the adoption of multi-objective optimization. Second, the optimization scheme prepared on the basis of parametric study indicates that the shortest life of a geothermal reservoir is 10 years, the injection-production pressure difference is up to 67 MPa, there is a significant thermal breakthrough phenomenon and the reservoir safety faces challenges. Third, by virtue of multi-objective optimization and decision making integration, the optimal operation parameter combination of hot dry rock system is determined, the life of geothermal reservoirs can exceed 20 years and balanced optimization is achieved. In conclusion, the idea of multi-objective optimization is feasible and applicable to geothermal energy exploitation and this method provides a reference for the efficient geothermal energy development and utilization and is helpful to the realization of “double carbon” goal in China.

Performance assessment and optimization of liquid desiccant dehumidifier system using intelligent models and integration with solar dryer

Realistic performance predictions are necessary to control the liquid desiccant dehumidification systems effectively. In the present study, the application of artificial intelligence (AI) based artificial neural network (ANN), gene expression program (GEP) and adaptive neuro-fuzzy inference system (ANFIS) is investigated to estimate the dehumidifier effectiveness of desiccant dehumidifier systems while considering the effect of moisture removal rate and sensible heat factor as performance characteristics. It is found that the AI-based GEP model has the best prediction capability compared to the other developed AI models. In addition, the sensitivity analysis of independent parameters on the system performance parameters is estimated using the cosine amplitude method. The results demonstrate that the inlet desiccant temperature and specific humidity have a more substantial influence on the dehumidifier effectiveness and sensible heat ratio. Further, an algorithm involving the combination of multi-objective particle swarm optimization (MOPSO) with the GEP model is developed to optimize the input process parameters and to achieve better dehumidification performance. Lastly, based on obtained optimal operating conditions, a case study on a dehumidifier-integrated solar dryer is proposed for food/agriculture products drying applications using AI-based GEP-MOPSO model. It is also observed that the inlet water temperature and solar intensity have a significant impact on the useful heat gain of the solar collector.

Optimization of collector area and storage volume in domestic solar water heating systems with on–off control—A thermal energy analysis based on a pre-specified system performance

Numerical simulations of solar water heating systems using on–off control were performed for four locations in the Portuguese territory, two collector types, and a wide range of parameters. Two main design parameters were considered: the collector area and the storage volume. An innovative technique was used to investigate the relationship between system performance and system parameters: a pre-specified annual solar fraction is selected and the optimal parameters needed to achieve this solar fraction are then computed. The optimum collector area is almost always higher for a double-glazed than for a single-glazed collector, suggesting that in Portugal, the single-glazed type is more effective for most solar water heating applications. This fact is more significant for lower solar fractions and warmer climates. A small increase in the optimum collector area leads to a very strong decrease in the optimum storage volume (an area increase of 1% leads to a volume decrease of 27%). Collector flow rate and storage volume increments result in lower collector areas. However, high flow rates lead to high pumping losses and lower collector efficiencies, and when the storage tank losses are significant, higher tank sizes may lead to higher collector areas. The solar fraction has a very strong impact on the collector area and storage volume (when the solar fraction increases from 0.5 to 0.99, the optimum collector area and storage volume increase about 17 and 56 times, respectively).

Systematic Performance Analysis of Hybrid FSO/RF System over Generalized Fading Channels with Pointing Errors

Hybrid free space optical (FSO)/radio frequency (RF) system has attracted extensive attention because of its advantages of both the FSO and RF links. From the viewpoint of overall system performance, this paper presents a systematic analysis method of communication performance and security performance of the hybrid FSO/RF system with the Málaga turbulence channel and the α−μ fading channel. The hybrid FSO/RF system adopts the diversity method of maximum ratio combining (MRC) to receive signals. The new expressions of communication performance parameters (i.e., the bit error rate, the outage probability, the ergodic channel capacity) of the only FSO system and the hybrid system are obtained. Then, the new expressions of the security performance parameters (i.e., the security outage probability and the strictly positive secrecy capacity) of the hybrid system with the FSO or RF links eavesdropping are derived, respectively. Our derived analytical expressions present an efficient tool to investigate the impact of system parameters on the overall performance of the hybrid system, namely modulation scheme, turbulence intensity, pointing errors, target rate, and eavesdropper output signal-to-noise ratio. The simulation results show that compared with the only FSO system, the hybrid system can significantly improve the communication performance of the system; the communication performance of the hybrid system using coherent binary phase shift keying (CBPSK) modulation is obviously better than the other two modulation technologies; with the deterioration of atmospheric environment (increasing turbulence intensity and pointing errors), the communication performance and security performance of the hybrid system will decline; both RF link eavesdropping and FSO link eavesdropping have a greater impact on the security performance of the hybrid systems; whether it is FSO link eavesdropping or RF link eavesdropping, the reduction of target rate and output signal-to-noise ratio of the eavesdropper can improve the security performance of the hybrid system.

Experimental Energetic and Exergetic Performance of a Combined Solar Cooking and Thermal Energy Storage System

Most solar cookers usually perform a single task of solely cooking food during sunshine hours. Solar cookers coupled with thermal energy storage (TES) material for off-sunshine cooking are usually expensive and require complex engineering designs, and cannot be used for dual purposes, for example, solar water heating and cooking. In this paper, a solar cooker that can perform dual tasks of cooking as well as storing thermal energy to be used during off-sunshine periods is presented. The experimental setup is composed of a parabolic dish, a solar receiver coupled with a flat plate and an oil-circulating copper coil for charging and discharging a storage tank. The objective of the experiment is to evaluate the energy and exergy thermal performance parameters of the dual-purpose system during charging and discharging cycles. The effect of the flow rate and the mass of the load are investigated while using sunflower oil as both the heat transfer fluid and the storage material. Charging and discharging experiments are conducted using four different flow rates (2, 3, 4, 5 mL/s), and with different masses (0.5, 1, 1.5, 2.0 kg) with water and sunflower oil as the test loads. The charging results show that the average energy and exergy rates as well as their corresponding efficiencies increase with an increase in the charging flow rate. On the other hand, the increase in the mass load tends to decrease marginally the average charging energy and exergy rates for water, and their corresponding efficiencies. For sunflower oil, the average charging energy and exergy rates and efficiencies showed a more pronounced decrease with an increase in the mass. Water generally shows higher charging and discharging energy and exergy efficiencies compared to sunflower oil with an increase in the flow rate. For discharging results, the correlations between the energy and exergy thermal performance parameters with respect to the flow rate and the heating load are not well defined possibly due to different initial storage tank temperatures at the onset of discharging and the inefficient discharging process which needs to be optimized in future.

Research on correlation model between transducer temperature and acoustic performance parameters of ultrasonic machining system

In the process of ultrasonic vibration cutting (UVC), the acoustic performance parameters of ultrasonic machining system change because of systems heating up and cutting loads. The changes of acoustic performance parameters will affect resonant frequency, impedance, and power match of the ultrasonic machining system, and stability of the amplitude of UVC system. It is hard to monitor the acoustic performance parameters online. Based on the analysis of the correlation mechanism between transducer temperature and acoustic performance parameters, the correlation models between transducer temperature and resonance frequency, static capacitance, and dynamic resistance of ultrasonic vibration machining system are established by curve regression analysis modeling method. The acoustic performance parameters of an ultrasonic vibration machining system are determined by transducer temperature using the correlation models. The effectiveness of the model is verified by experiments. It gives the information for the stability evaluation of the ultrasonic vibration machining process, the dynamic impedance matching of the ultrasonic machining system, and the power matching adjustment of the ultrasonic power supply.

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.