Energy Consumption Prediction And Optimal Allocation Of Pulverizing System In Large Coal-Fired Power Plant

In recent years, as the deterioration of the environment and the high energy consumption are highlighted, the country puts forward the important measures of energy saving and emission reduction.The coal-fired power plant, as a big consumer of energy, must closely follow the pace of national development, and actively put into the competition in the power market. In order to occupy a place in the fierce competition market, it is necessary to improve their own competitiveness and reduce the cost of power generation.Pulverizing system as the main auxiliary system in the coal-fired power plant, whose economic operation is an important way to achieve energy saving and consumption reduction in power plant. The main research object of this paper is the medium speed coal mill-positive pressure direct blowing type of cold primary air fan pulverizing system, which is currently widespread used in large coal-fired power generation units:firstly, analysis on the main energy equipment and energy consumption characteristics and statistics on the main operation mode of pulverizing system in coal-fired power plant; Followed by an analysis of the factors affecting the energy consumption of coal pulverizing system and building up the model according to the support vector machine (SVM) and chaotic particle swarm optimization algorithm to predict milling unit consumption, then combining with field trials to adjust the single factor of pulverizing system for studying its effect on energy consumption characteristics of boiler, and test data was used to verify the mathematical model; Finally according to multiple coal grinding machine in parallel operation characteristics, respectively using dynamic programming method and chaotic particle swarm optimization algorithm for optimal allocation, and MATLAB is applied to write programs. In fact, the optimized energy consumption of coal pulverizing system reduced obviously.Nowadays, not only the power plant automation control level is getting higher, but also more and more historical data is accumulated. It is very meaningful and valuable to find ways to improve the operation of the system from the operating data of the thermodynamic system. The paper, based on a large number of historical data in a coal-fired power plant and combined with field trials to research and analysis of the pulverizing system, then adopting chaotic particle swarm optimization algorithm for optimal allocation of the pulverizing system. It has a certain guiding significance for the optimal distribution of coal-fired power plant in the future.