Title :
Study on Exhaust Gas Temperature of Supercritical Boiler Based on LSSVM-GA
Author :
Liu Ding-ping ; Cai Hong-ming
Author_Institution :
Coll. of Electr. Power, South China Univ. of Technol., Guangzhou, China
Abstract :
High exhaust gas temperature of boiler would seriously affect the boiler efficiency. Due to the large thermal capacity and thermal parameters´ inertia in supercritical boiler, it was important to control accurately the exhaust gas temperature in the process of operation. The paper applied the Least Square Support Vector Machine (LSSVM) to build the studying model of exhaust gas temperature through analyzing its influencing factors, then made a sensitivity analysis of some factors.Lastly,the study used the method of genetic algorithm (GA) to optimize the exhaust gas temperature. The research obtained the optimizing and adjusting tactics, which have guiding significance in boiler´s control.
Keywords :
boilers; genetic algorithms; least squares approximations; support vector machines; LSSVM-GA; exhaust gas temperature; genetic algorithm; least square support vector machine; supercritical boiler efficiency; Algorithm design and analysis; Boilers; Feeds; Genetic algorithms; Least squares methods; Sensitivity analysis; Support vector machines; Temperature control; Temperature sensors; Thermal loading;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
DOI :
10.1109/APPEEC.2010.5449517
