Boiler Performance Optimization Using Fuzzy Logic Controller

Santoso, Handi M.H.; Nazaruddin, Yul Y.; Muchtadi, Farida I.

doi:10.3182/20050703-6-cz-1902.01780

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…This study also reviews previous studies that utilized AI to optimize the combustion efficiency of power-generation boilers. Santoso et al proposed an approach for designing a fuzzy logic controller for the optimization of the air-fuel ratio during the boiler combustion process and suggested boiler combustion efficiency improvement through the reduction of excessive air [24]. Liu et al proposed a method for improving the boiler combustion efficiency by integrating a non-dominated sorting genetic algorithm (NSGA2) of multi-objective optimization techniques with computational fluid dynamics (CFD) because conventional AI-based optimization techniques were limited, despite the fact that the boiler combustion process needs to be optimized to improve the efficiency of coal-fired power generation.…”

Section: Boiler Combustion Efficiency Improvement Using Aimentioning

confidence: 99%

“…While various studies have explored the application of machine learning and AI in optimizing combustion efficiency in power plant boilers , most have been limited to either focusing solely on combustion flue gas O 2 [19][20][21][22][23] or exclusively on boiler combustion efficiency optimization [24][25][26][27][28][29][30][31]. This research sets itself apart by deriving boiler efficiency optimization points using predictions of both combustion flue gas O 2 and CO levels, highlighting a scientific gap in existing research.…”

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confidence: 99%

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Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Lee,

Choi,

Lee

2023

Energies

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The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses.

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Section: Boiler Combustion Efficiency Improvement Using Aimentioning

confidence: 99%

mentioning

confidence: 99%

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Lee,

Choi,

Lee

2023

Energies

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“…If the mixture is, poor this means the emergence of CO in the products and this causes losses in the heat, this process called incomplete reaction. While if the mixture is a rich that refers to generate CO 2 in the products and this process called complete reaction [7]. Excess air very necessary to gets rid of heat loss and make the reactions complete.…”

Section: Air Fuel Ratio Input Variablementioning

confidence: 99%

“…[ ] Where: k is constant and equal to (0.9 for gas, 0.94 for oil and 0.97 for coal). For various fuels used in these reactions, Table explains the excess air that needed at full capacity as follows [7]:…”

Section: Air Fuel Ratio Input Variablementioning

confidence: 99%

Controlling for Boiler in Thermal Power Plants by Using Fuzzy Logic Controller

Anead¹,

Sultan²,

Ghafoor³

2017

ETJ

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Presently used modern techniques to control instead of the traditional control techniques for many industrial applications virtually or theoretically. In this research, a Fuzzy logic has been applied to control the important variables of steam generator in AL Dura station in Baghdad that generate (160 MW). These controlled variables are pressure, temperature ,air fuel ratio ,lower water level, flame and gas . Fuzzy requires a data, which obtained from actual power plant. This work explains the control on two stages during operation boiler and after operation boiler in order to make a right decision if any faults occurred during these stages at limit conditions based on fuzzy system. It was used simulation of Fuzzy system in MATLAB program. The results showed that the adoption of control technique that based on Fuzzy logic have a high response to indicate the control signals and thus can be depended as an active control system for selecting a right decisions compared with traditional systems. The adoption of fuzzy logic in control system gave the ability to take on decision for control signals with a high stability compared with traditional methods. The fuzzy system contributed in giving prophesies for station case to tell the operator what to do therefore enhancement the performance of station through to take a right decision to avoid stopping. The adoption of mfs in term (trapezoidal) showed a big corresponds with the proposed system and that is through a rapid response. The enhancement of efficiency was 10% when using fuzzy system. The accuracy of fuzzy in control was 0.99 that gave the ability to take on decision for control signals with a high stability compared with traditional methods.

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Optimization of Fuel in Saturated Steam Boiler through Preheating of Controlled Air-Fuel Mixture

Ibrahim

Farrukh

2019

2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

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Boiler Performance Optimization Using Fuzzy Logic Controller

Cited by 3 publications

References 1 publication

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Controlling for Boiler in Thermal Power Plants by Using Fuzzy Logic Controller

Optimization of Fuel in Saturated Steam Boiler through Preheating of Controlled Air-Fuel Mixture

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