A Novel Approach for Energy and Water Conservation by Using Silver-Carbon Quantum Dots Hybrid Nanofluids in Wet Cooling Towers Systems

This study presents the use of Silver-Carbon Quantum Dots(Ag-CQD)hybrid nanofluids,prepared by a facile wet chemical method,for heat transfer enhancement of wet cooling towers systems.The samples were characterized using different analyses,including FT-IR,XRD and TEM.After synthesizing the CQD,it was hybridized with silver nanoparticles and dispersed in water,using ultrasonic probe.The viscosity and density of the prepared nanofluid were investigated as a function of temperature and nanoparticles concentration,which demonstrated that there were no noticeable changes at lower particles concentration.Then,thermal conductivity and convective heat transfer coefficient were measured to evaluate the heat transfer enhancement of the nanofluid.At 45℃ and 0.5 wt%,the most significant thermal conductivity improvement compared to the base fluid was 24%;and 28%enhancement of the heat transfer coefficient was obtained at Reynolds number of 15529.The nanofluid performance was evaluated in a wet cooling tower for investigating the efficiency and water consumption rate.The results indicated that the efficiency of the cooling tower,by applying Ag-CQD nanofluid,enhanced from 23.72%to 28.23%;consequently,the amount of the consumed water decreased from 80.76 mL·min^(−1)to 69.67 mL·min^(−1).The results proved that the prepared nanofluid is a successful and promising candidate to enhance heat transfer.

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

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.