Fault detection and replacement of a temperature sensor in a cement rotary kiln

This paper proposes a method for fault detection and replacement of the sensor responsible by the measurement of the burning zone temperature in a rotary cement kiln. The control of the burning zone temperature is crucial for the control of kiln temperature and therefore for the control of the cement quality, pollutant emissions, and consumed energy. However the flying dust within the kiln can block the pyrometer sensor, causing faults in the temperature sensor. Exploring the analytical redundancy that usually exist in industrial processes, the proposed methodology uses a neural network trained using an online sequential extreme learning machine to online construct a model to estimate the burning zone temperature. Using the error between the measured and estimated temperatures, faults in the measurement can be detected and thus the replacement of the measured temperatures by the estimated output is made.

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