Optimization of Condenser Vacuum Based on Neural Network and SA-BBO

Lei Zhang; Ya Lan Ye; Feng Qi Si

doi:10.4028/www.scientific.net/AMR.860-863.676

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863Optimization of Condenser Vacuum Based on Neural...

Optimization of Condenser Vacuum Based on Neural Network and SA-BBO

Abstract:

The optimization of condenser vacuum is significant to improve efficiency and save energy in the power plant. Taking a 600MW unit as the research object, the condenser vacuum optimization model was established synthetically based on neural network, simulated annealing and biogeography optimization hybrid algorithm (SA-BBO). Circulating pumps power, slight increase of turbine power as well as the market value difference between coal and electric were included in the model. The objective function of the model is to maximize the profit of the power plant. The most effective combinations of the condenser vacuum and the circulating water pump were calculated eventually in different operating conditions by using characteristic analysis of variable condenser conditions. In a certain condition, running three circulating pumps for two steam turbines instead of two pumps can make the condenser vacuum reduce 0.49kPa, and increase revenue 110.2 yuan/h.

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Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

676-679

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.676

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Online since:

December 2013

Authors:

Lei Zhang, Ya Lan Ye, Feng Qi Si

Keywords:

Biogeography-Based Optimization Algorithm, Circulating Water Pump Operating Optimization, Condenser Optimum Vacuum, Neural Network (NN), Simulated Annealing (SA)

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