A method for predicting the performance of a condensing heat exchanger based on the partial load rate

Abstract

The invention discloses a method for predicting the performance of a condensing heat exchanger based on the partial load rate, comprising the following steps: step 1, establishing a non-linear mappingrelationship between a design variable and an objective function; 2, establishing a BP neural network structure for performance prediction; 3, training that BP neural network; Step 4, predicting thethermal efficiency, NOx and CO concentration of the condensing heat exchanger under the partial load rate by using the trained neural network. Using this method to predict the performance of condensing heat exchanger, the experiment operation is convenient, the data acquisition is simple, and the prediction accuracy is high. More importantly, the possibility of dangerous operation of condensing heat exchanger under overload and overload conditions can be effectively avoided.

Description

technical field [0001] The invention relates to a method for predicting heat exchanger efficiency and pollutant discharge concentration under partial load rate working conditions. Background technique [0002] As an important energy transfer equipment, heat exchangers are widely used in petroleum, chemical, energy and other industries. The development of more efficient and energy-saving heat exchangers and the study of more stable and efficient heat exchanger operation rules have become the research field of heat exchangers. hotspots. After the oil crisis in the Middle East in the 20th century, countries such as Europe and the United States began to conduct research on more efficient condensing gas-fired boilers and condensing heat exchangers, and proposed to reduce the exhaust gas temperature of the boiler to below the dew point temperature and recover the vaporization of a large amount of water vapor in the flue gas. Latent heat, thereby improving the energy utilization e...

AI Technical Summary

Problems solved by technology

Among the existing studies on the thermal efficiency and pollutant concentration of condensing water heaters, there are few studies on the prediction of water heaters under overload and overload conditions.

Even for the prediction research on the thermal efficiency and pollutant discharge of water heaters with a load rate of 30% to 100%, it is mainly based on experimental tests, and neural network technology is rarely used for prediction research

Moreover, when using experimental or theoretical methods, it is difficult to establish and find the nonlinear mapping relationship between the research object and the research objective function for the thermal efficiency and pollutant emission concentration of the water heater under the partial load rate condition.

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