Prediction method for dirt change trend of large condenser

Abstract

The invention provides a method for predicting the dirt change trend of a large condenser. The method comprises the steps as follows: 1) a support vector machine prediction structure is established, and two subnets are respectively used for predicting the change trends of soft dirt and hard dirt; 2) the product of the current working condition, the historical dirt degree, the cleaning period and the like of the condenser and proportional divisor is used as the input of a support vector machine, the support vector machine is used for obtaining the prediction values of the soft dirt and the hard dirt, and then the sum of the soft dirt and the hard dirt is multiplied with the proportional divisor to be used as a finial dirt prediction result. The proportional divisor can be adjusted by an online learning algorithm to be fit for different conditions. The invention can overcome the difficulty that the dirt is difficult to be described by an exact mathematic model, and the invention is applicable to prediction under different working conditions, is applicable to the prediction of condenser sets with different capacities, can satisfy the prediction of different time spans and has self learning capacity, thereby realizing the exact prediction of the dirt change trend of the condenser.

Description

technical field [0001] The invention belongs to the application field of artificial intelligence technology, and relates to a method for predicting the fouling trend of large condensers Background technique [0002] Condenser is a large-scale heat exchange equipment in the electric power, chemical industry and other industries. Its function is to condense the exhaust steam after the steam turbine has done work into water, reduce the exhaust steam pressure and exhaust temperature, and improve the thermal efficiency of the cycle. When the condenser is running, the cooling water flows through the heat exchange tubes, and the exhaust steam after the steam turbine has done work enters through the steam inlet, flows down along the gap between the heat exchange tubes, releases heat to the tube wall and condenses into water. Due to the unclean water quality of the cooling water, the inner wall of the heat exchange copper tube has accumulated dirt. The existence of dirt reduces the h...

AI Technical Summary

Problems solved by technology

Therefore, conventional prediction methods are difficult to accurately predict the change trend of fouling, and there are problems such as it is only suitable for prediction under certain operating conditions, or it can only predict short-term change trends, or it is only suitable for fouling prediction of certain capacity condenser units, etc.

Images