Novel system for predicting slagging behaviour of fuel blends in large scale utility boilers

Tan, Chee-Keong; Kakietek, Sławomir; Wilcox, S. J.; Ward, John; Golec, Tomasz; Szymczak, J.

doi:10.1179/174602206x152581

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Yang et al 9 established a slagging prediction method based on BP-ANN based on simple laboratory coal quality and ash grouping analysis data, which can easily predict the slagging possibility of pulverized coal. By measuring the industrial analysis and ash composition analysis of pulverized coal, Tan et al 10 based on an artificial neural network (ANN) and further optimized by a genetic algorithm (GA) obtained a neural network model for predicting slagging. Huang et al 11 established an ANN model to predict the biomass slagging trend by calculating six parameters such as acid compound ratio and silicon ratio and using multiple regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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The pre-combustion chamber (PCC) is commonly used to ensure stable combustion in boilers. However, when a coal-fired boiler uses a PCC combustor, the cross-sectional area and volumetric heat load in the PCC are high, which is prone to slagging, affecting the safe and stable operation of the boiler. Therefore, developing a fast and accurate prediction model is very important for judging the degree of slagging on the wall of the PCC. In recent years, artificial intelligence (AI) has been widely used in the field of thermal engineering, especially in the prediction of slagging. However, currently, using neural networks to predict the degree of boiler slagging only inputs simple parameters such as silicon ratio and acid–base ratio, without considering the actual complex flow and combustion characteristics in the furnace. In order to improve the accuracy of boiler slagging prediction, a deep parallel residual convolution neural network (DPRCNN) is proposed for automatically identifying three types of boiler wall slagging degrees. First, we simulate the boiler combustion process under various operating and structural parameters and output a dataset. Second, experimental validation is used to numerically simulate typical operating conditions, verifying the accuracy of the resulting dataset, and the generated dataset is sent to the DPRCNN model for identification. Finally, a convolutional neural network incorporating parallel thinking is proposed to predict boiler slagging. The experimental results show that the accuracy, accuracy, and area under curve (AUC) of DPRCNN reach 100%, 100%, and 100%, verifying the applicability of deep learning technology to boiler slagging prediction.

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Section: Introductionmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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Hydrodynamic characteristics prediction models for water-cooled wall under different loads based on Artificial neural network

Yang,

Zhang,

et al. 2025

Applied Thermal Engineering

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Improvement of hydraulic parameters of heat supply water heating boilers

Arifjanov,

Kurbanov,

Xoshimov

2024

E3S Web Conf.

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Practical and theoretical research has been carried out to optimize thermal energy use and incorporate energy-saving methods in consumption and production processes. This article presents an analysis of research on heat transfer by improving its hydraulic parameters in order to increase the energy efficiency of water heating boilers designed for heat supply of private residences and social facilities today. The research includes both practical experiments and theoretical analysis to ensure efficient thermal energy utilization. A program was developed to use the least squares method for analyzing test results on pipes of various cross-sections, aimed at evaluating the efficiency of heat transfer surfaces. The study found that pipes with corrugated inner walls in heat exchangers exhibited 15-20% higher efficiency compared to regular pipes, enhancing the plant's thermal energy efficiency. Implementing the proposed device for boiler pipes in manufacturing enterprises could allow for the construction of additional facilities, such as greenhouses, fruit drying shops, and service buildings. This innovation enables the production and use of low-cost hot water, potentially generating additional income in the production of bricks or porcelain.

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Novel system for predicting slagging behaviour of fuel blends in large scale utility boilers

Cited by 3 publications

References 4 publications

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Hydrodynamic characteristics prediction models for water-cooled wall under different loads based on Artificial neural network

Improvement of hydraulic parameters of heat supply water heating boilers

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