A Novel Approach for Energy Efficiency Prediction of Various Natural Draft Wet Cooling Towers Using ANN

Abstract

Cooling tower is crucial equipment in the cool-end system of power plant and the natural draft counter-flow wet cooling tower (NDWCT) gets wide application. The artificial neural network (ANN) technique is becoming an effective method for the thermal performance investigation of cooling towers. However, the neural network research on the energy efficiency performance of NDWCTs is not sufficient. In this paper, a novel approach was proposed to predict energy efficiency of various NDWCTs by using Back Propagation (BP) neural network: Firstly, based on 638 sets of field test data within 36 diverse NDWCTs in power plant, a three-layer BP neural network model with structure of 8-14-2 was developed. Then the cooling number and evaporation loss of water of different NDWCTs were predicted adopting the BP model. The results show that the established BP neural network has preferable prediction accuracy for the heat and mass transfer performance of NDWCT with various scales. The predicted cooling number and evaporative loss proportion of the testing cooling towers are in good agreement with experimental values with the mean relative error in the range of 2.11%–4.45% and 1.04%–4.52%, respectively. Furthermore, the energy efficiency of different NDWCTs can also be predicted by the proposed BP model with consideration of evaporation loss of water in cooling tower. At last, a novel method for energy efficiency prediction of various NDWCTs using the developed ANN model was proposed. The energy efficiency index (EEI) of different NDWCTs can be achieved readily without measuring the temperature as well as velocity of the outlet air.