Experimental studies of the combustion of pulverized coal to form the basis for training a neural network

Butakov, E. B.; Pochtar, A S; Vinogradov, Serguei V.; Burdukov, A. P.

doi:10.1088/1742-6596/2233/1/012016

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The articles of [79,80] present the results of experimental studies of the combustion of pulverized coal. The results obtained are then used to train the neural network.…”

Section: Mathematical Modeling-artificial Neural Network Approachmentioning

confidence: 99%

A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions

2022

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Research in the field of building mathematical models for combustion processes has been ongoing ever since the chemical reactions of combustion were first discovered. The authors of this review have systematized mathematical models of coal–water suspension (CWS) combustion processes, the sequence of analytical and experimental studies, and have also shown the global genesis of the CWS use. In addition, this review touches upon a topic that is inextricably linked with the combustion of CWS, namely their transportation from the place of coal mining to their place of thermal utilization. For developing countries, their own energy independence is in the foreground, as it is the basis for their economic independence and also a means for other sectors of their economy to be protected from the impact of market changes in fuel prices in the future spot world market. The authors of this review explored the possibility of using Kyrgyz brown coal and transporting it through a coal pipeline from a mountainous area to an industrial site for thermal utilization in specialized steam boiler units. As the economic analysis showed, for the conditions of the Republic of Kyrgyzstan, the use of CWS and coal pipelines with rising prices for natural gas is economically justified. The recommendations of the authors are used in scientific reports and methodological recommendations for the energy and mining sectors of the Republic of Kyrgyzstan, how the recommendations can also be applied to similar conditions in the highlands of Russia, China, and India.

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“…The articles of [79,80] present the results of experimental studies of the combustion of pulverized coal. The results obtained are then used to train the neural network.…”

Section: Mathematical Modeling-artificial Neural Network Approachmentioning

confidence: 99%

A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions

2022

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“…A number of works in this direction are devoted to attempting to formulate a method or algorithm for E3S Web of Conferences 459, 07012 (2023) https://doi.org/10.1051/e3sconf/202345907012 XXXIX Siberian Thermophysical Seminar estimating combustion process parameters based on analysis of individual characteristics of flame images. An example of the use of information on the spectral composition of radiation (flame colour) directly related to the chemical reactions taking place is the method of planar pyrometry and many others [12][13]. The main elements of the furnace are: a fuel supply system; a straight-flow burner located horizontally on the furnace axis; a combustion chamber with gauges and sight-glasses; a combustion chamber with tangential secondary air supply; and a smoke exhauster.…”

Section: Introductionmentioning

confidence: 99%

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

Butakov,

Abdurakipov

2023

E3S Web Conf.

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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.

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Experimental studies of the combustion of pulverized coal to form the basis for training a neural network

Cited by 2 publications

References 5 publications

A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions

A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions

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

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