Thermal power generating unit denitration system based on deep learning and optimal control method

A thermal power unit, deep learning technology, applied in neural learning methods, chemical instruments and methods, separation methods, etc., can solve the problems of air preheater scaling and corrosion, complex reaction mechanism, unsatisfactory and other problems

Pending Publication Date: 2021-03-30
山东纳鑫电力科技有限公司
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Problems solved by technology

There is a lag in the detection of flue gas parameter signals at the inlet and outlet of the SCR system, and there is a large delay in the reaction process of the SCR reactor. PID control can not get satisfactory control effec...

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  • Thermal power generating unit denitration system based on deep learning and optimal control method
  • Thermal power generating unit denitration system based on deep learning and optimal control method
  • Thermal power generating unit denitration system based on deep learning and optimal control method

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] Such as figure 1 As shown, the basic flow chart of the existing SCR denitrification system is given. The SCR flue gas denitrification system is arranged between the economizer and the air preheater. The flue gas temperature in this area is usually 300-500°C. The activity is stronger, which is more conducive to the oxidation-reduction reaction of ammonia and NOx. SCR flue gas denitrification system mainly includes ammonia injection grid, deflector, rectifier, catalyst, SCR reactor, etc. The flue gas is drawn from the horizontal flue at the outlet of the economizer, enters the vertical connection of the rising flue at the entrance of the SCR reactor, mixes with the ammonia gas injected from the ammonia injection grid, passes through the rectifier vertically downward, and passes through the SCR reactor. The arranged denitrification catalyst underg...

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Abstract

The invention discloses a thermal power generating unit denitration system based on deep learning, and the system comprises a NOx concentration target value setting unit, a dynamic matrix control DMCcontrol unit, a PID control unit, an ammonia injection valve, an SCR reactor, a hearth outlet NOx concentration prediction module unit and an intelligent feedforward controller unit. According to theoptimization control method, data modeling is carried out on the basis of historical data and real-time data of target power plant operation, boiler side adjustable parameters serve as input, SCR outlet NOx concentration measured values serve as output, and a prediction model is constructed through a least square support vector machine algorithm. According to the thermal power generating unit denitration system and the optimal control method, precise ammonia spraying control over the SCR system is achieved, the problems that the system response time is short, and parameter fluctuation is largeare solved, and the control quality of the system is ensured.

Description

technical field [0001] The present invention relates to a thermal power unit denitrification system and an optimal control method, more specifically, to a thermal power unit denitrification system and an optimal control method based on deep learning. Background technique [0002] Nitrogen oxides are one of the main sources of air pollution, and the nitrogen oxides emitted by thermal power plants account for about 35% to 40% of the national nitrogen oxide emissions. The energy conservation and emission reduction of thermal power units is in line with the current development trend of energy conservation and environmental protection. With the increasing awareness of environmental protection, the treatment of flue gas from thermal power units has attracted social attention. NOx is one of the main harmful substances in flue gas. [0003] In this context, my country's environmental protection department has issued a series of plans to curb NOx emissions from thermal power plants. ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/25G06N3/00G06N3/08G06N3/12B01D53/56B01D53/86G06F111/10
CPCG06F30/27G06N3/08G06F30/25G06N3/006G06N3/126B01D53/8696B01D53/8625B01D2251/2062G06F2111/10
Inventor 朱志军王涛雷文涛
Owner 山东纳鑫电力科技有限公司
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