Maintaining railway turnout operability is crucial for ensuring railway transport safety. Electric heating of railway turnouts is a significant technical and economic issue. The classical heating is characterised by high power consumption. For this reason, research is needed to optimise the current system. This paper presents results of a numerical analysis and of experimental researches. The numerical analysis was carried out using the ANSYS software. There was conducted a numerical comparative analysis of energy loss during heating performed using two different heaters. Including the classical method and a heater thermally insulated from a rail. In the first step, heating of a working space filled with a substitute snow model was considered. The snow-covered surface area was held within the working space of the turnout. It was assumed that the snow substitute material had thermal properties approximately the same as real light snow. It was also assumed that the material is in the solid state which would not undergo a phase change. In the next step, a real snow model that included the phase change process was taken into account. The energy efficiency and heat distribution in the turnout have been analysed and compared. The experimental researches were carried out in a physical model. The results showed that the use of a contactless heater results in creating a larger area over which emitted heat affected snow in the working space. Consequently, more snow was melted around the contactless heater than the classic one. This experimental observation supported the results of the numerical analyses presented previously.
The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.
The aim of the paper was an attempt at applying the time-series analysis to the control of the melting process of grey cast iron in production conditions. The production data were collected in one of Polish foundries in the form of spectrometer printouts. The quality of the alloy was controlled by its chemical composition in about 0.5 hour time intervals. The procedure of preparation of the industrial data is presented, including OCR-based method of transformation to the electronic numerical format as well as generation of records related to particular weekdays. The computations for time-series analysis were made using the author’s own software having a wide range of capabilities, including detection of important periodicity in data as well as regression modeling of the residual data, i.e. the values obtained after subtraction of general trend, trend of variability amplitude and the periodical component. The most interesting results of the analysis include: significant 2-measurements periodicity of percentages of all components, significance 7-day periodicity of silicon content measured at the end of a day and the relatively good prediction accuracy obtained without modeling of residual data for various types of expected values. Some practical conclusions have been formulated, related to possible improvements in the melting process control procedures as well as more general tips concerning applications of time-series analysis in foundry production.