Abstract
This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.
Highlights
Adding nanoparticles to a base fluid affects the thermophysical characteristics of the fluid [1,2,3,4,5]
Several studies are conducted on the effects of adding nanoparticles on the heat transfer of nanofluids [6,7,8,9,10].Wanatasanapan et al [11] investigated the influence of TiO2 -Al2 O3 nanoparticle mixing ratio on the thermal conductivity, rheological properties and dynamic viscosity of water-based hybrid nanofluid
The results indicated that increasing the nanoparticles volume fraction or Reynolds number caused enhancement of Nusselt number and convection heat transfer coefficient
Summary
Adding nanoparticles to a base fluid affects the thermophysical characteristics of the fluid [1,2,3,4,5]. Li et al [12] estimated the stability and thermal performance of Al2 O3 -ethylene glycol (EG) nanofluids under ultrasonic conditions. Their results showed that Al2 O3 -EG nanofluids obtained by ultrasonation for 60 min showed the most encouraging properties. Sekhar et al [13] prepared cobalt oxide-water nanofluid and studied its thermal and physical properties. Based on their results, relative viscosity values decreased with temperature and increased with the volume fraction of nanoparticles. Gu et al [14] evaluated thermal conductivity and viscosity properties of water-based nanofluid containing carbon nanotubes decorated with Ag nanoparticles
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