Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Tex

Predicting parameters of heat transfer in a shell and tube heat exchanger using aluminum oxide nanofluid with artificial neural network (ANN) and self-organizing map (SOM)

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 shelland-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.https://www.mdpi.com/journal/sustainabilityMechanical and Aeronautical Engineerin

Experimental study of Al2O3/water nanofluid turbulent heat transfer enhancement in the horizontal double pipes fitted with modified twisted tapes

In this study, fluid flow of the Al2O3 nanofluid in a horizontal double pipe heat exchanger fitted with modified twisted tapes were experimentally studied under turbulent flow conditions. The experiments with different geometrical progression ratio (GPR) of twists as the new modified twisted tapes and different nanofluid concentration were performed under similar operation condition. Pitch length of the proposed twisted tapes and consequently the twist ratios changed along the twists with respect to the geometrical progression ratio (GPR) whether reducer (RGPR 1). Regarding the experimental data, utilization of RGPR twists together with nanofluids tends to increase heat transfer and friction factor by 12% to 52% and 5% to 28% as compared with the tube with typical twisted tapes (GPR = 1) and nanofluid. Contrarily, performances were weakened by using for IGPR twists 0.6 to 0.92 and 0.75 to 0.95. The thermal performances of the heat exchanger with nanofluid and modified twisted tapes were evaluated for the assessment of overall improvement in thermal behavior. Generalized correlations were developed for the estimation of Nusselt number, friction factor and thermal performance factor under turbulent flow conditions. Satisfactory agreement between the present correlations and obtained experimental data validate the proposed correlations

Effect of Twisted-Tape Turbulators and Nanofluid on Heat Transfer in a Double Pipe Heat Exchanger

Heat transfer and overall heat transfer in a double pipe heat exchanger fitted with twisted-tape elements and titanium dioxide nanofluid were studied experimentally. The inner and outer diameters of the inner tube were 8 and 16 mm, respectively, and cold and hot water were used as working fluids in shell side and tube side. The twisted tapes were made from aluminum sheet with tape thickness (d) of 1 mm, width (W) of 5 mm, and length of 120 cm. Titanium dioxide nanoparticles with a diameter of 30 nm and a volume concentration of 0.01% (v/v) were prepared. The effects of temperature, mass flow rate, and concentration of nanoparticles on the overall heat transfer coefficient, heat transfer changes in the turbulent flow regime Re≥2300, and counter current flow were investigated. When using twisted tape and nanofluid, heat transfer coefficient was about 10 to 25 percent higher than when they were not used. It was also observed that the heat transfer coefficient increases with operating temperature and mass flow rate. The experimental results also showed that 0.01% TiO2/water nanofluid with twisted tape has slightly higher friction factor and pressure drop when compared to 0.01% TiO2/water nanofluid without twisted tape. The empirical correlations proposed for friction factor are in good agreement with the experimental data

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient

Comparison of the Experimental and Predicted Data for Thermal Conductivity of Fe3O4/water Nanofluid Using Artificial Neural Networks

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) training algorithm are used to predict the thermal conductivity of the nanofluid. Fe3O4 nanoparticles are prepared by chemical co-precipitation method and thermal conductivity coefficient is measured using 2500TPS apparatus. Results: Fe3O4 nanofluids with particle size of 20-25 nm are used to test the effectiveness of ANN. Thermal conductivity of Fe3O4 /water nanofluid at different temperatures of 25, 30 and 35℃ and volume concentrations, ranging from 0.05% to 5% is employed as training data for ANN. The obtained results show that the thermal conductivity of Fe3O4 nanofluid increases linearly with volume fraction and temperature. Conclusions: the artificial neural network model has a reasonable agreement in predicting experimental data. So it can be concluded the ANN model is an effective method for prediction of the thermal conductivity of nanofluids and has better prediction accuracy and simplicity compared with the other existing theoretical methods

Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: Dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, and radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM)

Nanofluid Thermal Conductivity Prediction Model Based on Artificial Neural Network

Heat transfer fluids have inherently low thermal conductivity that greatly limits the heat exchange efficiency. While the effectiveness of extending surfaces and redesigning heat exchange equipments to increase the heat transfer rate has reached a limit, many research activities have been carried out attempting to improve the thermal transport properties of the fluids by adding more thermally conductive solids into liquids. In this study, new model to predict nanofluid thermal conductivity based on Artificial Neural Network. A two-layer perceptron feedforward neural network and backpropagation Levenberg-Marquardt (BP-LM) training algorithm were used to predict the thermal conductivity of the nanofluid. To avoid the preprocess of network and investigate the final efficiency of it, 70% data are used for network training, while the remaining 30% data are used for network test and validation. Fe2O3 nanoparticles dispersed in waster/glycol liquid was used as working fluid in experiments. Volume fraction, temperature, nano particles and base fluid thermal conductivities are used as inputs to the network. The results show that ANN modeling is capable of predicting nanofluid thermal conductivity with good precision. The use of nanotechnology to enhance and improve the heat transfer fluid and the cost is exorbitant.It can play a major role in various industries, particularly industries that are involved in that heat

Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions

In this work, the electrical conductivity of CuO/glycerol nanofluid was measured at a temperature range of 20–60 °C, volume fraction of 0.1–1.5% and nanoparticle size of 20–60 nm. The experimental data were predicted by the perceptron neural network. The results showed that the electrical conductivity increases with temperature, especially in higher volume fractions. These results are attributed to the accumulation of nanoparticles in the presence of the field and their Brownian motion at different temperatures and the reduction of electrical conductivity at higher nanoparticle sizes is attributed to the decreased mobility of nanoparticles as load carriers as well as to their decrease in volume unit per constant volume fraction. The results revealed that sonication time up to 70 min increases the nanofluid stability, while further increase in the sonication time decreases the nanofluid stability. In the modeling, input data to perceptron artificial neural network are nanofluid temperature, nanoparticle size, sonication time and volume fraction and electrical conductivity is considered as output. The results obtained from self-organizing map (SOM) showed that the winner neuron which has the most data is neuron 31. The values of the correlation coefficient (R2), the mean of squared errors (MSE) and maximum error(emax) used to evaluate the perceptron artificial neural network with 2 hidden layers and 31 neurons are 1, 2.3542 × 10−17 and 0 respectively, indicating the high accuracy of the network

Development of a Community-Based Care System Model for Senior Citizens in Tehran

In recent years a variety of system models for providing community based health care services for the elderly has been envisioned and implemented in the developed countries which meet the special care needs of different groups of elders. In Iran as a developing country, a considerable percentage of old people live in the society need to receive specialized care and health services. In order to respond to these care needs, developing and implementing health and social care systems with consideration of relevant factors such as: existing and available resources and facilities (financial and human resources); social and cultural issues and characteristics of each groups of elders(healthy elders, frails,home bounds, …)  seem to be necessary. At the development phase of the care model in this study Ellowing methodological triangulation must be considered: 1) Comprehensive review of current and related literature, 2) Conduction of an ethnographic study on a number of Tehranian elders and their families, 3) Seeking opinions of a group of experts on this issue using nominal group technique, and analysis as well as synthesis of the collected data were employed to develop a community based care system for elders. The preliminary results of employing this care system and examination of expected outcomes such as promoting quality of life and expectancy in elders reflects the efficiency of this system, although further complementary studies and particularly cost benefit analysis are strongly recommended