Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions

Grochowalski, Jaroslaw; Jachymek, Piotr; Andrzejczyk, M.; Klajny, Marcin; Widuch, Agata; Morkisz, Paweł; Hernik, Bartłomiej; Zdeb, Janusz; Adamczyk, Wojciech

doi:10.1016/j.energy.2021.121538

Cited by 26 publications

(5 citation statements)

References 14 publications

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“…The error was observed to be 1.08%, 1.23%, and 1.87% for ST, SP, and SSFR, respectively. The difference between experimental and predicted values indicated a deviation from the fitting line, which might be positive or negative [31]. The current study observed a higher number of negative residuals, which might be due to the limitation in data size.…”

Section: Discussionmentioning

confidence: 46%

Performance Evaluation and Parametric Optimization of Coal-Fired Water Tube Boiler Using the Grey-Taguchi Method

Talib,

Yasin,

Hussain

et al. 2023

International Journal of Energy Research

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Industries, district heating companies, and public institutions that use boilers for heating, processing, or power production find it challenging to run at peak efficiencies due to rising fuel prices. Insufficient heat energy production and distribution through boilers contribute to an overall increase in energy expenditure. The performance of a boiler is affected by various controlling parameters, including specific fuel consumption capacity, load, and heat losses. The current study was conducted to evaluate the performance of the coal-fired water tube boiler at D.G. Khan Cement Company Limited, Pakistan. The experimental results were validated with artificial neural network- (ANN-) based predictions, which were observed to have an error of 14% in the regression plot. In this study, the performance parameters of the boiler, including steam temperature (ST), steam pressure (SP), and specific steam flow rate (SSFR), were optimized against fuel consumption (FC) and load using the Grey-Taguchi method. The best-performing parameters, with the best criteria, were observed at an overall grey relational grade (OGRG) of 0.891 and a load of 66%. The findings indicated that the overall performance of the boiler was optimized with an FC of 3.09 kg/s, a load of 66%, ST of 532°C, SP of 9.93 MPa, and SSFR of 21.38 kg/s.

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Section: Discussionmentioning

confidence: 46%

Performance Evaluation and Parametric Optimization of Coal-Fired Water Tube Boiler Using the Grey-Taguchi Method

Talib,

Yasin,

Hussain

et al. 2023

International Journal of Energy Research

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“…In Ref. [178], a CFBB gasifier/steam turbine/proton exchange membrane (PEM) fuel cell integrated system was developed. The system contains many thermochemical, biochemical, and physical processes.…”

Section: Algorithmmentioning

confidence: 99%

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Liang¹,

Guo²,

Chen³

et al. 2023

CAAI Artificial Intelligence Research

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This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

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“…The circulating fluidized bed was suggested as it offers strong fuel flexibility with good efficiency and performance factors. The construction of the latest CFB boilers allows stable and relatively low temperatures in the combustion chamber, e.g., the average flue gas temperature can be maintained in the range of 800-900 • C for minimization of thermal nitrogen oxide (NO x ) formation and avoiding the issues of low ash melting temperatures [86,87], which is particularly important for some types of animal-origin waste. The high content of nitrogen in poultry litter and cattle waste may be a source of increased NO x emission [88], thus the application of advanced deNO x methods may be required [89].…”

Section: Direct Combustionmentioning

confidence: 99%

Significance and Challenges of Poultry Litter and Cattle Manure as Sustainable Fuels: A Review

Maj

2022

Energies

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Growing animal production results in a significant amount of waste, composed of manure, bedding, feed, feathers, etc., whose safe and cost-effective disposal becomes a troublesome challenge. The literature review points out that the higher heating value (HHV) of animal-origin waste reaches 19 MJ/kg (dry basis), which positions it as a promising renewable energy source. Various paths of energy recovery were investigated in the literature, but the thermal processes, particularly combustion and co-combustion, were indicated as the most effective from both technical and environmental points of view. The presented study reviews the fuel characteristics, possible combustion-related challenges, and ash disposal routes of the most popular animal-origin waste: poultry litter and cow (cattle) manure with a slight sight on piggery (swine) manure. When considering animal-origin feedstock as fuel, usually only animal species is given (poultry, cattle, etc.). However, according to the analyzed literature data, this is not sufficient information. Several more factors crucially influence the fuel and ash properties of animal waste and the most vital are: the housing system, type of bedding, and farming style. Animal litter is considered a “difficult” fuel, nevertheless, it does not always cause combustion-related problems. Some analyzed feedstock feature low chlorine concentrations and high ash melting temperatures, which makes them combustion-friendly.

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Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions

Cited by 26 publications

References 14 publications

Performance Evaluation and Parametric Optimization of Coal-Fired Water Tube Boiler Using the Grey-Taguchi Method

Performance Evaluation and Parametric Optimization of Coal-Fired Water Tube Boiler Using the Grey-Taguchi Method

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Significance and Challenges of Poultry Litter and Cattle Manure as Sustainable Fuels: A Review

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