Oxidation fan operation optimization algorithm based on particle swarm optimization

Abstract

The invention relates to the technical field of flue gas desulfurization of a coal-fired power plant, and particularly discloses an oxidation fan operation optimization algorithm based on particle swarm optimization, which comprises the following steps: S1, finding out influence factors influencing the operation efficiency of an oxidation fan according to historical operation data, and establishing a data set, S2, carrying out correlation preprocessing on the data set in S1 by adopting a Pearson correlation analysis method, s3, selecting operation parameters related to the oxidation rate and the gypsum quality as input, taking the oxidation rate and the gypsum quality as output, and establishing an oxidation rate LSTM prediction model and a gypsum quality LSTM prediction model, S4, taking the oxidation rate and the field operation parameters as references, establishing a mechanism model, obtaining a comprehensive cost model of the desulfurization system under various working conditions, and calculating the comprehensive cost of the desulfurization system. S5, performing single-target optimization on the oxidation air volume by adopting a particle swarm optimization algorithm on the basis of the LSTM prediction model, the gypsum quality LSTM prediction model and the desulfurization system comprehensive cost model; and S6, searching the optimal oxidation air volume and providing the optimal oxidation air volume to an oxidation fan control system.

Description

technical field [0001] The application relates to the technical field of flue gas desulfurization in coal-fired power plants, and specifically discloses an oxidation fan operation optimization algorithm based on particle swarm optimization. Background technique [0002] After the desulfurization system of thermal power plants has been retrofitted with ultra-low emissions, the energy consumption has increased to a certain extent. Oxidation fans, as the major energy consumers of the desulfurization system, have considerable energy-saving potential. At present, the main problem restricting the energy saving of the oxidation fan is the inaccurate measurement of the concentration of sulfite in the slurry of the desulfurization absorption tower and the existence of hysteresis. The routine sulfite content detection method is chemical periodic sampling test, laboratory analysis data, and report the test results once a day, and the desulfurization operator on duty adjusts the operat...

AI Technical Summary

Problems solved by technology

This operation mode has a long lag time and cannot adapt to the rapid changes in load and sulfur content. As a result, the desulfurization operator on duty must rely on experience to adjust the oxidation air volume, which is prone to two problems: low air volume control may lead to deterioration of slurry quality and environmental protection Over-emission accident; high air volume control leads to increased power consumption of the oxidation fan, which is not energy-saving

[0003] The existing technical solution is: Roots fans are mostly used for the oxidation fans of the wet desulfurization system at present, and the oxidation air volume is controlled according to the quality of gypsum or operating experience, which has the disadvantages of long feedback time and slow speed, and most of the oxidation fans are constant. Speed ​​fan, as long as the desulfurization device is running, the fan will run accordingly. Regardless of the state of the desulfurization system, the fan will run at a constant speed and constant energy consumption. When the desulfurization system changes, the oxidation fan will still provide the designed oxidation air volume. , resulting in increased energy consumption, and causing too much excess air in the absorption tower slurry to easily generate bubbles, resulting in false liquid levels in the absorption tower, resulting in abnormal events such as slurry overflow. In view of this, the inventor proposed a particle-based Group Optimal Oxidation Fan Operation Optimization Algorithm

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