Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network

Journal of Thermal Analysis and Calorimetry - Nanofluids are employed in different thermal devices due to their enhanced thermophysical features which lead to noticeable heat transfer augmentation....     

Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles

Journal of Thermal Analysis and Calorimetry - Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids...     

Thermal resistance modeling of oscillating heat pipes filled with acetone by using artificial neural network

Journal of Thermal Analysis and Calorimetry - Oscillating heat pipes (OHPs) are applicable in different energy systems such as solar collectors, desalinations and fuel cells as thermal mediums or...     

QSPR modeling of decomposition temperature of energetic cocrystals using artificial neural network

Journal of Thermal Analysis and Calorimetry - The quantitative structure–property relationship for the decomposition temperature (Td) of energetic cocrystals was investigated. The artificial...     

Performance evaluation of a U-shaped heat exchanger containing hybrid Cu/CNTs nanofluids: experimental data and modeling using regression and artificial neural network

Journal of Thermal Analysis and Calorimetry - In the current research, viscosity and thermal conductivity of hybrid Cu/CNTs water-based nanofluids were investigated at various concentrations of...     

Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy

Journal of Thermal Analysis and Calorimetry - In the present study, a comprehensive model based on least square support vector machine algorithm (LSSVM) was developed to estimate thermal...     

Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis

Instrumentation spectra used for chemometrics analysis are often too unwieldy to model, as many of the inputs do not contain important information. Several mathematical methods are used for reducing the number of inputs to the significant ones only. Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs. However, using a non-random initial connection weight algorithm and local minima avoidance and escape techniques can overcome these difficulties. Once the ANN model is trained, the analysis of its connection weights can easily identify the more relevant inputs. Repeating the process of training the ANN model with the reduced input set and the selection of the more relevant inputs can proceed until a quasi-optimal, small, set of inputs is identified. Two examples are presented—finding the minimal set of wavelengths in benchmark diesel fuel NIR spectra, and in spectra generated in a recent work, modeling of “artificial nose” sensor array. In the last example, 1260 inputs were reduced to optimal sets of <10 inputs. Causal index calculation can analyze the influence of each of selected wavelengths on the predicted property. Some of the resulting minimal sets are not unique, depending on the ANN architecture used in the training. The accuracy of the resulting ANN models is usually better, and more robust, than the original large ANN model.     

Thermal conductivity and specific heat capacity measurements of CuO nanofluids

A study of thermal properties of CuO dispersed in water and ethylene glycol as a function of the particle volume fraction and at temperatures between 298 and 338 K has been performed. Thermal conductivities have been studied by the steady-state coaxial cylinders method, using a C80D microcalorimeter (Setaram, France) equipped with special calorimetric vessels. Heat capacities have been measured with a Micro DSC II microcalorimeter (Setaram, France) with batch cells designed in our laboratory and the “scanning or continuous method.” Results for thermal conductivities can be well justified using a classical model (Hamilton–Crosser), and experimental measurements of heat capacities can be justified with a model of particles in thermal equilibrium with the base fluid.     

Quantification of fructo-oligosaccharides based on the evaluation of oligomer ratios using an artificial neural network

The application of an internal standard in quantitative analysis is desirable in order to correct for variations in sample preparation and instrumental response. In mass spectrometry of organic compounds, the internal standard is preferably labelled with a stable isotope, such as ¹⁸O, ¹⁵N or ¹³C. In this study, a method for the quantification of fructo-oligosaccharides using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI TOF MS) was proposed and tested on raftilose, a partially hydrolysed inulin with a degree of polymeration 2-7. A tetraoligosaccharide nystose, which is chemically identical to the raftilose tetramer, was used as an internal standard rather than an isotope-labelled analyte. Two mathematical approaches used for data processing, conventional calculations and artificial neural networks (ANN), were compared. The conventional data processing relies on the assumption that a constant oligomer dispersion profile will change after the addition of the internal standard and some simple numerical calculations. On the other hand, ANN was found to compensate for a non-linear MALDI response and variations in the oligomer dispersion profile with raftilose concentration. As a result, the application of ANN led to lower quantification errors and excellent day-to-day repeatability compared to the conventional data analysis. The developed method is feasible for MS quantification of raftilose in the range of 10-750 pg with errors below 7%. The content of raftilose was determined in dietary cream; application can be extended to other similar polymers. It should be stressed that no special optimisation of the MALDI process was carried out. A common MALDI matrix and sample preparation were used and only the basic parameters, such as sampling and laser energy, were optimised prior to quantification.     

Forecasting the thermal conductivity of a nanofluid using artificial neural networks

Journal of Thermal Analysis and Calorimetry - In this study, the influence of incorporating MWCNT on the thermal conductivity of paraffin was evaluated numerically. Input variables including mass...     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

Simultaneous spectrophotometric determination of phosphate and silicate by using principal component artificial neural network

A very sensitive, simple and selective spectrophotometric method for simultaneous determination of phosphate and silicate based on formation of phospho- and silicomolybdenum blue complexes in the presence of ascorbic acid is described. Although the complexes of phosphate and silicate with reagent in the presence of ascorbic acid show a spectral overlap, they have been simultaneously determined by principal component artificial neural network (PC-ANN). The PC-ANN architectures were different for phosphate and silicate. The output of phosphate PC-ANN architecture was used as an input for silicate PC-ANN architecture. This modification improves the capability of silicate PC-ANN model for prediction of silicate concentrations. The linear range was 0.01-3.00 microg mL(-1) for phosphate and 0.01-5.00 microg mL(-1) for silicate. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of phosphate and silicate in detergents.     

Analysis of ultrasonic velocity in refrigerants using artificial neural network

Several researchers have reported numerous measurements on ultrasonic velocity as a function of temperature and pressure using various experimental techniques. A large amount of experimental data is required in order to obtain accurate results for the chemical substances used. The present article explores the evaluation of ultrasonic velocity as a function of molecular weight, temperature and pressure using an artificial neural network (ANN) in six refrigerants. The network so developed predicts the ultrasonic velocity successfully. Statistical analysis of the results was performed using standard deviation (%) and relative average deviation. The correlation coefficient in our analysis was found to be 0.9999. The trained weights, obtained from ANN, are further employed to form equations to predict ultrasonic velocity at other temperatures and pressures.