Real-Time Boiler Control Optimization with Machine Learning

Real-Time Boiler Control Optimization with Machine Learning

JD DIGITALIZING CHINA'S THERMAL POWER PLANTS …

JD won the bid thanks to its boiler combustion optimizing control system. The AI control system, automatically adjusts a range of variables for thermal boilers in real time, including the coal feeding process, air distribution and water vapour levels, according to JD

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Modeling and Optimization of NOX Emission in a Coal …

1/1/2014 · A new methodology combining the advanced extreme learning machine (ELM) and harmony search (HS) was proposed to model and optimize the operational parameters of the boiler for the control of NO X emissions in a 700 MW pulverized coal-fired power plant. emissions in a 700 MW pulverized coal-fired power plant.

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Deep Bidirectional Learning Machine for Predicting NOx …

Combustion optimization is one of the effective techniques to enhance boiler efficiency and reduce nitrogen oxide (NOx) emissions from coal-fired boilers. A precise NOx emission model and a boiler efficiency model are the basis of implementing real-time combustion optimization and are required. In this study, to obtain very precise models and make full use of abundant real-time operational

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Real-Time Boiler Control Optimization with Machine …

7/3/2019 · Real-Time Boiler Control Optimization with Machine Learning. 03/07/2019 ∙ by Yukun Ding, et al. ∙ 0 ∙ share. In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of

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Optimization of Thermal Efficiency and Unburned Carbon in Fly Ash of Coal-Fired Utility Boiler …

Y. Zhao et al.: Optimization of Thermal Efficiency and Unburned Carbon in Fly Ash of Coal-Fired Utility Boiler via GWO Algorithm FIGURE 1. Flowchart of the proposed ANN-GWO method. The main contributions of this paper focus on the following two aspects.

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Modeling and reduction of NOX emissions for a 700 MW …

1/1/2016 · The novel ELM (extreme learning machine) model was introduced to model the correlation between operational parameters and NO X emissions of the boiler. Approximately ten days of real data from the SIS (supervisory information system) of a 700 MW coal-fired power plant were acquired to train and verify the ELM-based NO X model.

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Combustion optimization of a coal-fired boiler with …

15/10/2014 · Fast learning network (FLN) is a novel double parallel forward neural network, which proves to be a very good machine learning tool. However, some randomly initialed weights and biases may be non-optimal performance parameters. Therefore, for the problem, this paper proposes a double linear fast learning network (DLFLN), in which all weights and biases are divided into two parts and each part

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Optimizing combustion efficiency of a circulating …

1/1/2005 · A data mining approach was applied to analyze relationships among 54 parameters of a circulating fluidized-bed boiler. Knowledge was extracted from the data by machine learning algorithms. The extracted knowledge was used to determine ranges of process parameters (control signatures) that led to the increased efficiency of the combustion process. The research has shown that the efficiency …

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Deep Bidirectional Learning Machine for Predicting NOx …

Combustion optimization is one of the effective techniques to enhance boiler efficiency and reduce nitrogen oxide (NOx) emissions from coal-fired boilers. A precise NOx emission model and a boiler efficiency model are the basis of implementing real-time combustion optimization and are required.

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Optimizing Boiler Control in Real-Time with Machine …

In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient

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Combustion efficiency optimization and virtual testing: a …

In this paper, a data-mining approach is applied to optimize combustion efficiency of a coal-fired boiler. The combustion process is complex, nonlinear, and nonstationary. A virtual testing procedure is developed to validate the results produced by the optimization methods. The developed procedure quantifies improvements in the combustion efficiency without performing live testing, which is

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[1903.04958] Real-Time Boiler Control Optimization with …

7/3/2019 · In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm

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Real-Time Boiler Control Optimization with Machine …

In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm

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TC-CPS - CUHK CSE

Real-Time Boiler Control Optimization with Machine Learning Yukun Ding, Yiyu Shi University of Notre Dame 1 Introduction As coal-fired power plants currently produce 41% of global electricity [31], proper control of coal-fired boilers

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