The throttling flowmeter used in utility boilers has been used as a measuring instrument for a long time. However, its safety performance, such as welding, lacks enough attention. The manufacturing welds of the throttling flowmeter are welded with austenitic stainless steel and pearlitic heat-resistant steel. Cracks and other defects are generally found in the inspection and detection of the manufacturing welds of throttling flowmeters in service, which have serious potential accidents. To find out the relationship between weldingdissimilar steels and defects, the microhardness indentation method was used to measure the residual stress of welding. Combined with the self-developed calculation software of microscopic indentation residual stress distribution, we used the difference between the color of the indentation area and the surrounding area to quickly measure the indentation strain collected by the microscope using the computer image recognition algorithm, which greatly improved the accuracy and speed of the microscopic indentation residual stress test. The results show that the residual stress at the weld of dissimilar steel is relatively large, and the mechanical property test is generally unqualified. The microindentation residual stress analysis method based on computer image recognition is used to quickly, intuitively, and accurately reveal the direct relationship between the combination of dissimilar steel welding materials and the generation of cracks and other defects.
Centralized heating is an energy-saving and environmentally friendly way that is strongly promoted by the state. It can improve energy utilization and reduce carbon emissions. However, Centralized heating depends on accurate heat demand forecasting. On the one hand, it is impossible to save energy if over producing, while on the other hand, it is impossible to meet the heat demand of enterprises if there is not enough capacity. Therefore, it is necessary to forecast the future trend of heat consumption, so as to provide a reliable basis for enterprises to reasonably deploy fuel stocks and boiler power. At the same time, it is also necessary to analyze and monitor the steam consumption of enterprises for abnormalities in order to monitor pipeline leakage and enterprise gas theft. Due to the nonlinear characteristics of heat load, it is difficult for traditional forecasting methods to capture data trend. Therefore, it is necessary to study the characteristics of heat loads and explore suitable heat load prediction models. In this paper, industrial steam consumption of a paper manufacturer is used as an example, and steam consumption data are periodically analyzed to study its time series characteristics; then steam consumption prediction models are established based on ARIMA model and LSTM neural network, respectively. The prediction work was carried out in minutes and hours, respectively. The experimental results show that the LSTM neural network has greater advantages in this steam consumption load prediction and can meet the needs of heat load prediction.
The flow rate in a closed pipe is a dynamic value, the instrument for measuring the flow rate is called a flowmeter. Typical flowmeters are: differential pressure flowmeter, electromagnetic flowmeter, coriolis mass flowmeter, vortex flowmeter, ultrasonic flowmeter, etc. Among them, differential pressure flowmeter, represented by orifice flowmeter, is the mainstream flowmeter applied at home and abroad. Orifice flowmeter has the advantages of simple structure, low cost and stable performance, it is widely used in chemical industry, electric power, heating, water supply and other fields, and can be used to measure the flow of different media. When the fluid filled with pipeline flows through the orifice plate, it will produce local contraction, concentration of flow beam, increase of flow velocity, and decrease of static pressure, so there will be a static pressure difference before and after the orifice plate. The orifice flowmeter is equipped with an orifice plate on the pipeline, and the orifice plate is connected with pressure measuring tubes on both sides, and they are respectively connected with a U-type pressure differential meter. Orifice flowmeter uses the throttling effect of fluid through the sharp hole to increase the flow velocity and decrease the pressure, resulting in the pressure difference between front and back orifice plates, as the basis of measurement. In this paper, to study the influence of different pressure loads on the accuracy of the orifice flowmeter, the mathematical model is established by simulation software, and the different pressure loads of the orifice flowmeter are analyzed under the temperature load of 20°C. The results show that the location of the orifice flowmeter under the maximum stress, maximum displacement and maximum strain under the pressure load of 0.1 MPa is different from that under the pressure load of 50 MPa and 100 MPa. When the pressure loads are respectively 0.1 MPa, 50 MPa, and 100 MPa, the location of maximum stress and displacement of the orifice flowmeter are the same.
Nozzle flowmeter has simple structure and wide application. In order to study the influence of temperature change on the inner wall of nozzle flowmeter on the thermal effect of flowmeter, the thermal effect of nozzle flowmeter was numerically simulated at five different inner wall temperatures in this paper. It is found that with the increase of the inner wall temperature, obvious stress concentration occurs in the upstream and downstream pressure inlets and the inlet and outlet areas of the flowmeter, and the thermal stress increases with the increase of the wall temperature. There are large thermal deformation areas in the inlet and outlet areas of the upstream and downstream pressure inlets and the eight-slot nozzle, and the thermal deformation value increases with the increase of wall temperature.
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