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Coal-fired units based on data fusion grindability line detection


776 * reflect the huge thermal power engineering of the time period 2009 Trends in coal. Table 23.4 and 8 months of coal into the furnace of the month the monthly mean data of total moisture /% dry basis ash /% low heat / Ⅲ ・ kg a 39.738517.848823.329649.6227l8.588622.9305822.930519. 2422.5826 statistics from the table 2 shows, with the increase in the month, the increase in the dry basic ash, water is also a slight increase in the average calorific value is declining. The trend shows a gradual deterioration of coal quality, 8-month and 3 month of coal are quite different. Table 2, the changes in coal quality and also the quality of domestic coal the same trend, that is, with rising coal prices and coal increased in the mixing material, the quality gradually declined. Meanwhile, August is greater than the loss of ground in March, so the trend in Figure 3 is right,[link widoczny dla zalogowanych], to reflect the broad changes in grindability. 3.3 grindability can wear line monitoring of changes to determine the size of a standard must first have an evaluation. Table 1 shows that, in January and February to the coal volume and little change in the relationship between the current mill, can be considered in January and February coals little change, while grinding the wear is very small, stable units. So with this in January and February of the K value of the statistical curve as the base curve, as shown in Figure 5, the line, which points to the actual calculation of the amount of coal under different K values calculated for each point 5min. Exponential curve of the figure represented in the form, and its expression is: = 22.8084 × B 'Lion (6) Figure 51 months and 2 months of the value of statistical curve type (6) as the base, using equation (4) real-time computation K value, calculated in real time, compared with baseline values, the difference in different regions for some time, you can determine the cycle time rate of coal mill changes, that is, the output time constant change. Take the year at 00:00 on March 10 to 16:00 time period data, 5min mean for a number of samples,[link widoczny dla zalogowanych], the 16h of the calculation results shown in Figure 6. Line l is the statistical norm K; line real-time calculation of the K 2 value. Can be seen from Figure 6, line 2 to line l is always a period of time the data showed high after a good agreement in the middle of the process, while always low. After other data to determine, control is no problem, the same amount of coal,[link widoczny dla zalogowanych], the power consumption of the different changes that cycle rate, you can determine the general coal grindability changed. 4 Conclusion Figure 6 curve grinding through different months of the statistical analysis, the mill motor current is not only a relationship with the amount of coal, but also by the impact of coal and its wear, the mill motor current can to some extent reflect the changes in coal grindability and grinding wear changes. The Mechanism of variation and found that coal pulverizing mill wear to the result is the same dynamic, are increasing the inertia time constant, and therefore proposed the concept of generalized grindability, coal mill will result in changes within the circulation ratio inertia time constant of change leaving the factors collectively referred to as generalized grindability. Grinding mill motor current use of memory and the relationship between the amount of coal grindability given the generalized expressions and criteria. According to the generalized grindability index of the calculated results of time-line amendment to optimize inertia dynamic wind / coal ratio between the limited space between the specific amendments to the follow-up article will be described. In this article, Dr. North China Electric Power University Foundation (200812009) References: [1] Tian Liang, had Deliang, Liu Xin Ping, et al. 500Mw unit simplified nonlinear dynamic model [J]. Power Engineering, 2004,24 (4) :522-525. [2] Zhao Zheng, PROCEEDINGS,[link widoczny dla zalogowanych], Tian Liang. The amount of fuel based on data fusion of soft-line measurement and correction of coal heat [J]. Thermal Power Engineering,[link widoczny dla zalogowanych], 2007,22 (1) :42-45. [3] in London Hi, Xu Hou, AESTHETIC, et al. Grindability index of coal-fired artificial neural network [J]. Coal Technology, 2003,22 (9) :91-93. [4] have Deliang, Zhao Zheng, Chen Yan bridge, and so on. 500MW unit boiler model and experimental analysis [J]. Proceedings of the CSEE, 2003,23 (5) :149-452. [5] Liu Fuguo. Coal boiler into the furnace of real-time monitoring of water [J]. Boiler technology, 21303,34 (6) :12-14. [6] have Deliang, Cuize Peng, Tian Liang, et al. Based on gray relational and D-S combination rule mill power plant fault diagnosis [J]. Power Engineering, 2007,27 (2) :207-210. (Edit He Jingfang)
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