The application of machine learning techniques to detect combustion modes in a pulverised coal boiler

Kutateladze Institute of Thermophysics, 630090 Novosibirsk, Russia

^* Corresponding author: e_butakov@mail.ru

Abstract

The development of machine learning algorithms based on semi-industrial thermal benches will approach the development of an automated system capable of detecting and tweaking energy-efficient and environmentally friendly combustion modes in large power plants and increasing their efficiency without significant changes in the design of boiler equipment. Determination of combustion modes and optimisation of the combustion process based on neural network analysis of visualisation patterns of the coal flame in the boiler. Determining the combustion mode in the furnace space and superimposing (automatically adjusting) the parameters based on sensor readings to bring it to the optimum mode and maintain stable combustion is a complex task. Currently, the selection of necessary parameters is done by operator-assisted automatic process control systems, but this process is based on known design parameters and is not always efficient or environmentally friendly in practice. This problem can be solved by determining the combustion mode in the furnace space using modern machine learning methods and automatic parameter optimisation in a continuous mode article.