원문정보
초록
영어
Boiler efficiency and emission load of NOX are the key evaluation indicators of operation performance of the coal-fired boiler. It has become a popular academic research topic concerning the reduction of NOX emissions while maintaining the same boiler efficiency, as well as how to build a model for boiler emission. Based on a prediction model that is constructed for boiler efficiency and emission load of NOX with the application of deep belief algorithm, genetic algorithm is used to optimize the tilting angel of boiler burners and the flow velocity of pulverized coal, thereby effectively reducing the emission load of NOX. Simulation results indicate that this method effectively optimizes the parameters of the boiler, and provides a new way to optimize the parameters of the boiler.
목차
1. Introduction
2. DBN Network
2.1. RBM Network
2.2. DBN Network
2.3. Improvements of the DBN Network
3. Output Forecast of Coal-Fired Boiler
3.1. Coal-Fired Boiler and its Influencing Factors
3.2. Index Prediction and Analysis
4. Optimization Parameters of Genetic Algorithm
4.1. Overview of the Algorithm
4.2. Algorithm Settings
4.3. Results of Optimization
5. Concluding Remarks
References