The prediction of heat efficiency and pollutant emission of non-rated loaded condensing heat exchangers
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In order to study the working conditions of condensing heat exchangers under non-rated load conditions, the orthogonal test method was conducted on the condensing gas water heaters by experiment firstly in this paper. Eighty groups of the experimental data about the relationship between the design variables and the object functions were obtained, among them, the design variables include excess air coefficient, flue gas flow, water flow and water flow temperature, and the objective functions include heat exchanger efficiency, NOx and CO emission concentration. The above experimental data were divided into two groups for the training and prediction of neural networks. Here two neural networks of BP artificial neural network (BPNN) and generalized regression neural network (GRNN) will be used to predict the heat efficiency and pollutant emission of condensate heat exchanger at partial load rate. The results show that both BPNN and GRNN structures achieve the required prediction accuracy, and the prediction accuracy of GRNN is higher than that of BPNN. The condensing heat exchanger can achieve higher heat exchanger efficiency and lower concentration of NOx and CO emission at the 90–100% load rate. When the load rate is lower than 30% or higher than 100%, the heat efficiency of the heat exchanger is decreased, and the concentration of pollutant emission is increased rapidly.
KeywordsCondensing heat exchanger Neural network Underload Overload
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Conflict of interest
The authors declare that they have no conflict of interest.
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