Abstract

This study effort contracts with measuring the flame temperature in the combustion chamber along with a scheme to monitor the temperature of flue gases from the power plants. Presently, the gases from the exhaust are monitore dusing gas analyzers which are positioned at the exit point of the chimney. These gas analyzers are prone to cold end corrosion which will lead to malfunctioning of the equipment causing erroneous results during measurements. Thiskind of indigenous technique facilitates to develop an Industrial IoT (IIoT) temperature monitoring system at power plants. A combination of Principal Component Analysis (PCA) with Cascaded Neural Network (CNN) is used. The features extracted and reduced serve as inputs to CNN. An efficient technology to scrutinize and measure the temperature inside the combustion chamber thereby reducing the amount of flue gas emissions by the partnership of two methods.

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