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coal pulverizer - mitsubishi heavy industries, ltd. global

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a comparison of three types of coal pulverizers

Pulverizers have three functions, pulverization, drying and classification. The classification function plays the important role of ensuring that only fine particles of the pulverized coal are sent to the boiler. Within a rotating-type classifier, fine particles are passed through while coarse particles are eliminated by utilizing the relative balance of centrifugal force and centripetal force produced by a blade rotating at several dozen rpm and the flow of air

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a comparison of three types of coal pulverizers

coal mill - an overview | sciencedirect topics

To control the quality of coal being sent to the burners located on the furnace walls. The word quality here means the temperature and fineness of the PF. The set temperature values are dependent on the percentage of volatile matter that exists in the main fuel. The controlled temperature is important for many reasons such as stability of ignition, better grindability of solid fuels, better floating ability of suspended PF particles, etc. However, a temperature more than ∼65 to 70 is not recommended for various reasons

Operating data from a coal mill is used to compare the fault detection observer-based method and PCA/PLS models based approach. There are 13 process measurements available representing different temperature, mass flows, pressures, speed etc in the coal mill

The measurement is not updated, if the variation is less than 1%. The variations of T(t) is in the major part of the operational time inside this interval. Therefore, it is not suitable to be chosen as the predictor variable. However, the variations can be extracted from the TPA(t), which is used to control the temperature of the mill. Therefore, the PLS model is developed with the temperature of the mill as the dependent variable. In addition 6 of the other variables are chosen as regressors since there is barely information in the remainder

coal mill - an overview | sciencedirect topics

A static PCA model is first developed, which captures around 99% of variations with 5 PCs (see Fig. 5), which indicates strong collinearity among regressors. As shown in Fig. 6, both Q and T2 statistics (with 95% confidence level) of the static PCA model are noisy, which potentially lead to false alarms. A static PLS model with 2 LVs achieves the minimal PRESS (see Fig. 7), which is applied to the test dataset. Fig. 8 shows the comparison between process measurement and the static PLS model prediction, together with the 95% confidence level. The process gradually drifts away form the NOC model, which eventually moves beyond the threshold around the sample 150. Due to the noise involved in the prediction signal, the estimation moves in and out the threshold from 110 till 200, when it is clearly out of the confidence level. Both Figs. 6 and 8 reveal that static PCA and PLS models may lead to false alarms due to the noisy estimation. In addition, process measurements are commonly auto-correlated, this behavior is expected since the coal mill runs dynamical. Thus, dynamic models are developed by including time lagged process measurements, to address the issue of auto-correlations and reduce the possibility of false alarms due the none modeled dynamics

Including time lagged terms enhance the NOC model by including historical data. However, time lagged terms also introduce additional noise into the modeling data block. For example, including n + 1 time lagged terms might lead to poorer validation performance than the model with n terms due to measurement noise. Therefore, PRESS is used to choose an appropriate number of time lagged terms for a dynamic PLS model

The predictive ability of the PLS model is improved with the inclusion of time lagged terms. The PRESS decreases from 1.645 to the minimal value of 1.142, which is obtained with a dynamic PLS of 3 LVs using 8 time lagged terms. The application of the dynamic PLS model to the test data reveals that the fault occurs in the process around sample 160. Fig. 9 also shows a much smoother prediction such that the possibility of false alarms is significantly reduced. A dynamic PCA model is developed by the inclusion of 8 time lagged terms. The number of PCs is chosen as 2 through cross-validation, which explains 70.6% of process variations. The Q statistic of the dynamic PCA model is shown in Fig. 10, the fault is detected around 160 samples, which is consistent with the dynamic PLS model

coal mill - an overview | sciencedirect topics

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