Artificial intelligence for control and optimization of boilers’ performance and emissions: A review

Nemitallah, Medhat A.; Nabhan, Mohammad A.; Alowaifeer, Maad; Haeruman, Agus; Alzahrani, Fahad; Habib, Mohamed A.; Elshafei, Moustafa; Abouheaf, Mohammed I.; Aliyu, Mansur; Alfarraj, Motaz

doi:10.1016/j.jclepro.2023.138109

Cited by 24 publications

(5 citation statements)

References 129 publications

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“…The authors also reported other improvements such as a 40–60 °F reduction in economizer exit gas temperature, a 5% decrease in ID fan power, and 120–180 Btu/kWh savings achieved with the hybrid methodology . AI-enhanced soot-blowing techniques mark a significant departure from conventional methods, as they are designed to integrate real-time data from fouling measurement devices. , This integration tailors fouling control to the current state of the boiler, making it particularly effective in situations where fouling is a recurring concern or when coal quality exhibits substantial variations.…”

Section: Fuel Combustion Improvement Strategiesmentioning

confidence: 99%

“…89 AI-enhanced sootblowing techniques mark a significant departure from conventional methods, as they are designed to integrate real-time data from fouling measurement devices. 150,151 This integration tailors fouling control to the current state of the boiler, making it particularly effective in situations where fouling is a recurring concern or when coal quality exhibits substantial variations.…”

Section: Advanced Mitigation Techniquesmentioning

confidence: 99%

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Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Bankefa,

Nasah,

Laudal

et al. 2024

Energy Fuels

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While coal−biomass cocombustion has gained considerable attention as a climate-sensitive power generation strategy, the detrimental impact of inorganic species present in low-rank fuels can lead to counterproductive outcomes in combustion systems. Low-rank solid fuels negatively affect heat transfer efficiency and energy conversion during steam generation, resulting in adverse environmental consequences. However, a combination of improved combustion technology and fuel quality improvements can enable the safe utilization of these fuels. This review highlights the pressing need for research in fuel quality improvement as an effective climate change mitigation strategy during the global transition to renewable energy sources. Identifying the problematic species in solid fuels and understanding the mechanisms of ash formation are critical steps toward designing effective strategies to control ash aerosol-induced fouling. The paper discusses methods for promoting clean combustion, including fuel pretreatment and cocombustion. Additionally, the review examines recent advancements in fuel improvement strategies, such as CFD-targeted-in-furnace injection (CFD-TIFI), intelligent soot blowing, and fuel additive technology, with a focus on mineral additives. Due to its high efficiency, low cost, and minimal retrofit requirements, additive technology holds promise for large-scale applications, paving the way for fuel flexibility in utility-scale power generation. The current study emphasizes the importance of fuel ash mineral transformation and characterization techniques for evaluating the effectiveness of fuel additives. Assessing the viability of utility-scale applications economically and the feasibility of a large-scale demonstration requires further research.

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Section: Fuel Combustion Improvement Strategiesmentioning

confidence: 99%

Section: Advanced Mitigation Techniquesmentioning

confidence: 99%

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Bankefa,

Nasah,

Laudal

et al. 2024

Energy Fuels

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“…• Problem 1: Insufficient reliability of O2 and CO analyzer [6]; • Problem 2: Combustion air is not controlled [7];…”

Section: Problem Statement and Objectivesmentioning

confidence: 99%

“…Problem 1: Insufficient reliability of O 2 and CO analyzer [6]; • Problem 2: Combustion air is not controlled [7]; • Problem 3: Absence of flue gas control point for optimizing boiler combustion efficiency [8].…”

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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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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“…This development holds great promise for the research community, offering the potential for highly accurate simulations that demand significantly reduced execution times and computational resources compared to traditional methods. Neural networks have already demonstrated their utility in effectively simulating diverse combustion processes and equipment including but not limited to spark ignition engines, 1 compression ignition engine, 2 chemical kinetics, 3 optical diagnostics, 4 gas turbine, 5 boilers, 6 burners, 7 rapid compression machines (RCMs), 8 and shock tubes. 9 A thorough comprehension of ignition delay allows engineers and researchers to optimize engine design and fuel formulations, leading to more efficient and environmentally friendly combustion processes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

Ind. Eng. Chem. Res.

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This review explores machine learning approaches for predicting ignition delay in combustion processes. Ignition delay is a vital parameter in optimizing the engine design, fuel formulations, and combustion efficiency. The review examines the applications of artificial neural networks (ANNs) and convolutional neural networks (CNNs) in various combustion processes and equipment, such as engines, boilers, and rapid compression machines. The differences between ANNs and CNNs are discussed, highlighting their capabilities and limitations. Numerous studies are presented, demonstrating the successful application of neural networks in predicting ignition delay for different fuels and engines. Overall, machine learning approaches show great promise in accurately predicting the ignition delay and advancing energy utilization.

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Artificial intelligence for control and optimization of boilers’ performance and emissions: A review

Cited by 24 publications

References 129 publications

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

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

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

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