Application of convolutional networks to detect the operating phases of energy systems using a biomass boiler as an example

  • 1 AGH University of Krakow, Poland


The development of neural algorithms opens new perspectives for the analysis of technological processes. Particularly relevant are strongly nonlinear and complex objects, such as power plants. One of the modern solutions enabling data analysis are convolutional neural networks (CNNs). The research presents the application of CNNs to monitor and optimize combustion processes in biomass boilers. The fuel analyzed was gray straw, which is difficult to control due to the nature of combustion. The proposed technique is based on the processing of temporal data, which represent different stages of the combustion process. The work examined the effectiveness of the model in identifying key operating parameters and detecting the stages of firing from ignition initialization to nominal operation. Analysis of images of parameter curves from the time waveforms makes it possible to capture repeatable relationships that enable faster response to future changes in the conditions of the combustion process. Determining the phase of the process, based on data and trends of selected parameters, allows the control system to react faster, without operator intervention. As a result of the study, the efficiency of process stage change detection by the convolutional network, expressed by means of an error matrix, through the F1-score parameter (harmonic mean between precision and sensitivity) was achieved at a level close to 96%. The proposed solution can be effectively applied to a number of technological processes including those that are part of Industry 4.0 effectively influencing technological transformation..



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