Modeling and Optimization of NO&lt;i&gt;&lt;sub&gt;x&lt;/sub&gt;&lt;/i&gt; Emission from a 660 MW Coal-Fired Boiler Based on the Deep Learning Algorithm

Wang, Yingnan; Xie, Ruibiao; Liu, Wenjie; Yang, Guohui; Li, Xinli

doi:10.1252/jcej.21we004

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…The second step is to optimize the three objectives and obtain corresponding target values, and then use the obtained target The study introduces a quality coefficient for quantification, and the relationship between the quality coefficient and the six major quality characteristics can be expressed through calculation expressions. The method for determining quantitative quality based on the following characteristics of engineering projects, including applicability, safety, economy, safety and environmental protection, durability, and reliability, is represented by the letters a1-a6 [18]. Quality quantification can control the quality of construction projects during the construction process through quality coefficients.…”

Section: Optimization Control Model For Construction Engineering Mana...mentioning

confidence: 99%

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Zhou,

Guo

2024

SCPE

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Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.

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Section: Optimization Control Model For Construction Engineering Mana...mentioning

confidence: 99%

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Zhou,

Guo

2024

SCPE

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Multi-objective prediction for denitration systems in cement: an approach combining process analysis and bi-directional long short-term memory network

Hao

et al. 2022

Environ Sci Pollut Res

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Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

Zuo,

Niu,

et al. 2024

Applied Sciences

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Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.

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Modeling and Optimization of NO<i><sub>x</sub></i> Emission from a 660 MW Coal-Fired Boiler Based on the Deep Learning Algorithm

Cited by 3 publications

References 33 publications

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Multi-objective prediction for denitration systems in cement: an approach combining process analysis and bi-directional long short-term memory network

Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

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