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Improving effectiveness turbine was and remains a key issue for today. In order to improve the efficiency of the turbine is necessary to reduce losses in the steam turbine exhaust conduit.This paper presents the design optimization exhaust conduit steam turbine K-27-2.9 produced by JSC «KTW» at the design stage. The aims of optimizing the design were: decreasing hydraulic resistance of the conduit, reduction of non-uniformity of the flow at the outlet of the conduit, equalizing steam flow ahead of the condenser tube bundle.The conduit models were made and flows in it were simulated in environment of the Solid Works and its application COSMOS Flo Works.As the initial conduit model was selected exhaust conduit of turbine PT-25/34-3.4 produced by JSC «KTW». Was obtained by the calculated velocity field at the outlet of the conduit. The analysis of the calculation results revealed the necessity of changes to the initial design of the conduit. The changes were accompanied by calculating currents flow in the conduit, and assessed the impact of design changes on the nature of the course. Further transformation of the construction of the conduit was held on the results of these calculations. Construction changes are not touched by the outer geometry of the conduit, and were introduced to meet technological.According to calculation results, conclusions were drawn and selected three versions of the conduit.Given are the research results for the initial conduit model and modified design versions. In order to evaluate the flow degree of irregularity the momentum factor (Bussinesku factor) for outlet crosssection of the selected conduit design version. Analysis of the research results made it possible to determine optimum design of the exhaust conduit.Introducing the suggested alterations in the conduit design will result in improvement of heat exchange in the condenser, an increase in reliability of the tube bundle operation, a decrease in noise and power losses, as well as a rise of the turbine plant efficiency in general.

To reduce the condensation pressure of the refrigerant in the summer, refrigeration system has been developed, in which, during periods of high air temperature, the heat of condensation is removed to the coolant, which was pre-cooled at night due to radiative cooling. A methodology has been developed for determining the main characteristics of the elements of the proposed system and calculating its daily energy consumption. The calculation shows that the proposed system with a nominal refrigerating capacity of 10 kW, using the R404a refrigerant, allows in the climate of the city of Shymkent to reduce the condensation temperature to +32.9°C, and daily energy consumption by 6.5% compared to an ordinary vapor compression refrigeration machine.

Abstract District heating systems are widely used to supply heat to different groups of heat consumers. The district heating system offers great opportunities for combined heat and power production. In this paper decreasing district heating supply temperature is analysed in the context of combined heat and power plant operation. A mathematical model of a CHP plant is developed using both empirical and theoretical equations. The model is used for analysis of modified CHP plant operation modes with reduced district heating supply temperature. Conclusions on the benefits of new operation modes are introduced.

AbstractAccording to the industry ecology theory, in the simulating flowing way between substance and energy in the nature ecology, to design the industrial production systems can use less resource reasonably and reduce emissions. As the most important large iron & steel enterprise in Inner Mongolia, applying the theory of industrial ecology, the Baogang Group gains great achievement in energy saving and emission reduction in recent years. Based on the industrial ecology theory, this paper mainly analyzes the practice of energy-saving and ejection-decreasing of the BaoGang Group.

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

The operating point of a propeller hydropower station will deviate from the effective workspace while the discharge reduces excessively during dry seasons. It usually leads to a decrease in efficiency and even to being unable to work. To solve the above problem, a scheme named decreasing capacity to increase efficiency was presented in this article. A low-specific-speed propeller runner with fixed blades that has the same installing dimensions as the original one was redesigned and equipped in dry seasons. A positive circulation at the outlet of the blades bigger than in conventional runners is allowed. Some key technologies about hydraulic design for runner blades were researched, which include distribution of velocity circulation at the inlet and outlet of the runner, thickening of the epiphyseal line of an aerofoil, unfolding aerofoil being converted to a cylindrical section, etc. In the section on digital modeling for runner blades, aerofoils on the cylindrical sections at the rim and at the hub were constructed employing the trend extrapolation method. Moreover, a blade digital model was built at one time according to the aerofoils on all cylindrical sections by means of a successful redevelopment to UniGraphics, and it has perfect symmetry. A case presented indicates that the method of decreasing capacity to increase efficiency is feasible. Using the method, the turbine efficiency increased from less than 28.6% to 83.4% while the discharge decreased from 3.20 m3s−1 to 1.00 m3s−1, and then the hydropower unit was able to work properly.

In intelligent lithium-ion battery management, the state of health (SOH) of battery is essential for the batteries’ running in electric vehicles. Popularly, the battery SOH is estimated by using suitable features and data-driven methods. However, it is difficult to extract appropriate features characterizing battery SOH from the charging and discharging data of batteries owing to various state of charges (SOCs) and working conditions of batteries. In order to effectively estimate the battery SOH, an estimation method based on gradual decreasing current, double correlation analysis and gated recurrent unit (GRU) is proposed in this paper. Firstly, gradual decreasing current in the constant voltage charging phase is measured as the raw data. Then, the double correlation analysis method is proposed to select combined features characterizing the battery SOH from different categories of features. Meanwhile, the number of input features is also ensured by the method. Finally, the GRU algorithm is employed to set up a SOH estimation model whose learning rate is improved by using a sparrow search algorithm (SSA) for the purpose of capturing the hidden relationship between features and SOH. The adaptability of the proposed method is validated by SOH estimation experiments of a single battery and a battery pack. Additionally, contrast experiments are performed to show the advanced estimation performance of the proposed method.

With the rapid development of electric energy storage, more and more attention has been paid to the accurate construction of energy storage lithium-ion battery (LIB) model and the efficient monitoring of battery states. Based on this requirement, a simulated annealing-back propagation (SA-BP) model is proposed, and the long-term state of health (SOH) of LIBs can be estimated online by combining with the battery single particle (SP) model. Among them, simulated annealing (SA) algorithm is used to optimize the initial parameters of back propagation (BP) network. In order to improve the identification efficiency and avoid the local optimization, the nonlinear decreasing step-bacterial foraging optimization (NDS-BFO) algorithm is introduced into the parameter identification process. On the basis of adopting the SOH sequence as the output of the SA-BP model, two electrochemical parameter sequences are used as the input of the model for training and testing. In addition, in this paper, the contributions in terms of the SOH estimation task mainly include two aspects. Firstly, the SOH estimation results can provide suggestions for the timely replacement of batteries in actual energy storage power stations. Secondly, the electrochemical parameters identified before SOH estimation are strongly related to the quality of the LIB. Therefore, they can provide references for the economy of LIBs. At 25 °C, the accuracy of the SP model is verified under three different working conditions. Degradation experiments are carried out under a constant current condition and a self-designed energy storage condition. The experimental results show that, under the 0.5 rate constant current condition, the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the long-term SOH estimation result are 0.42 %, 0.34 % and 0.38, respectively. And under the self-designed energy storage condition, the RMSE, MAE and MAPE of the result are 0.33 %, 0.26 % and 0.29, respectively. Under the same working ...