Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network

The effect of nanofluid on the cooling performance and pressure drop of a jacked reactor has experimentally been investigated. Aqueous nanofluids of Al₂O₃ and CuO was used as the cool ant inside the cooling jacket of the reactor. The application of the artificial neural networks (ANNs) to predict the performance of a double-walled reactor has been studied. Different architectures of artificial neural networks were developed to predict the convective heat transfer and pressure drop of nanofluids. The experimental results are used for training and testing the ANNs based on two optimal models via feed-forward back-propagation multilayer perceptron (MLP). The comparison of statistical criteria of different network shows that the optimal structure for predicting the convective heat transfer coefficient is the MLP network with one hidden layer and 10 neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The predicted pressure drop values by the MLP network with two hidden layers and 6 neurons in the each layer has been used from LM training algorithm, which showed a reasonable agreement with the experimental results.     

Artificial neural network model of molten carbonate fuel cells: Validation on experimental data

This article shows the teaching processes of artificial neural networks that are used to model the molten carbonate fuel cell (MCFC). Researchers model MCFCs to address a variety of issues across a range of complexities, from simply gauging the effect of temperature through to a complete model with 14 input parameters. The architecture of the model is a triple layer network with one hidden layer containing three neurons. The activation function used for the hidden layer was a hyperbolic tangent, with the last layer being based on linear function. We produced various network configurations, mostly networks containing one hidden layer. Models map the work of a real fuel cell with an average error in the range of 2.4% to 4.6%. The model we created guided the optimization of the thermal‐flow and construction parameters of the MCFC. Commercially available software was used to build the model and optimize the operating parameters. The selected objective functions were the efficiency of electricity production and the power density obtained from the cell's surface. The results obtained serve as pointers for possible changes in fuel cell operation and could lead to some structural changes being made.     

Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey

The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli, south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003–2005) were used as training data and the values of 2006 were used as testing data.In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg–Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R²) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction.     

Inverse neural network for optimal performance in polygeneration systems

In this paper, inverse neural network (ANNi) is applied to optimization of operating conditions or parameters in energy processes. The proposed method ANNi is a new tool which inverts the artificial neural network (ANN), and it uses a Nelder-Mead optimization method to find the optimum parameter value (or unknown parameter) for a given required condition in the process. In order to accomplish the target, first, it is necessary to build the artificial neural network (ANN) that simulates the output parameters for a polygeneration process. In general, this class of ANN model is constituted of a feedforward network with one hidden layer to simulate output layer, considering well-known input parameters of the process. Normally, a Levenberg–Marquardt learning algorithm, hyperbolic tangent sigmoid transfer-function, linear transfer-function and several neurons in the hidden layer (due to the complexity of the process) are considered in the constructed model. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown input parameter using the Nelder-Mead algorithm. ANNi results on three different applications in energy processes showed that ANNi is in good agreement with target and calculated input data. Consequently, ANNi is applied to determine the optimal parameters, and it already has applications in different processes with a very short elapsed time (seconds). Therefore, this methodology can be useful for the controlling of engineering processes.     

Modeling the effect of non-linear process parameters on the prediction of hydrogen production by steam reforming of bio-oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R²) > 0.9. A low mean square error of 3.024 × 10⁻²⁴ and 6.22 × 10⁻¹⁵ for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.     

Imperative role of neural networks coupled genetic algorithm on optimization of biohydrogen yield

Neural networks coupled genetic algorithm was applied to optimize the four key fermentation parameters (medium pH, glucose to xylose ratio, inoculum age and its concentration) for biohydrogen yield using mixed anaerobic microbial consortia. L16 orthogonal array (OA) was used for wet lab experimentation. The biohydrogen yield values differed with experimental conditions. The data was analyzed initially using neural network for finding out the effectiveness of experimental data. A 4-10-1 network topology was found to be effective indicating 10 neurons in the hidden layer. The observed R² value was 0.9999 indicating good approximation of prediction capability of employed neural network. The input and output training data revealed overall MAE of 3.38 × 10⁻⁸, MAPE of 2.81 × 10⁻¹⁰ and MSE of 9.1 × 10⁻⁸ indicating accuracy of the experimental and predicted values. Optimum conditions were determined by using genetic algorithm after evaluation of data for 300 generations and four best possible conditions were selected and validated the same. Overall, the biohydrogen yield was improved from 325 to 379 ml g⁻¹ substrate.     

Estimation of crack propagation in polymer electrolyte membrane fuel cell under vibration conditions

In transportation applications, the main reasons of mechanical damage in polymer electrolyte membrane fuel cell (PEMFC) are road-induced vibrations and impact loads. The most vulnerable place of these cells is the interface between membrane and catalyst layer in the membrane electrode assembly (MEA). Hence, studies on mechanical strength of PEMFC should focus on that interface. The objective of present study lies in the fact that employing a prediction method to investigate the damage propagation behavior of vibration applied PEMFC using artificial neural network (ANN). The data available in the literature are used to constitute an ANN model. Three-layer model; input, hidden and output, are used for construction of ANN structure. Initial delamination length (a), amplitude (A), frequency (ω) and time (t) are used as input neurons whereas delamination length is output. Levenberg–Marquardt algorithm is selected as learning algorithm. On the other hand, number of hidden layer neuron is decided with the use of different neuron numbers by trial and error method. It is concluded that prediction capability of ANN model is in allowable limits and model can be suggested as efficient way of delamination length estimation.     

Assessment of producer gas composition in air gasification of biomass using artificial neural network model

Energy generation from renewable and carbon-neutral biomass is significant in the context of a sustainable energy framework. Hydrogen can be conveniently extracted from biomass through thermo-chemical conversion process of gasification. In the present work, an artificial neural network (ANN) model is developed using MATLAB software for gasification process simulation based on extensive data obtained from experimental investigations. Experimental investigations on air gasification are conducted in a bubbling fluidised bed gasifier with different locally available biomasses at various operating conditions to obtain the producer gas. The developed artificial neural network consists of seven input variables, output layer with four output variables and one hidden layer with fifteen neurons. The multi-layer feed-forward neural network is trained employing Levenberg–Marquardt back-propagation algorithm. Performance of the model appraised using mean squared error and regression analysis shows good agreement between the output and target values with a regression coefficient, R = 0.987 and mean squared error, MSE = 0.71. The developed model is implemented to predict the producer gas composition from selected biomasses within the operating range. This model satisfactorily predicted the effect of operating parameters on producer gas yield, and is thus a useful tool for the simulation and performance assessment of the gasification system.     

Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr + H₂O concentrations. For the network, a feedforward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R > 0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder–Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system.     

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network’s training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.     

Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks

In this work an artificial neural network (ANN) is used to correlate experimentally determined and numerically computed Nusselt numbers and friction factors of three kinds of fin-and-tube heat exchangers having plain fins, slit fins and fins with longitudinal delta-winglet vortex generators with large tube-diameter and large the number of tube rows. First the experimental data for training the network was picked up from the database of nine samples with tube outside diameter of 18 mm, number of tube rows of six, nine, twelve, and Reynolds number between 4000 and 10,000. The artificial neural network configuration under consideration has twelve inputs of geometrical parameters and two outputs of heat transfer Nusselt number and fluid flow friction factor. The commonly-implemented feed-forward back propagation algorithm was used to train the neural network and modify weights. Different networks with various numbers of hidden neurons and layers were assessed to find the best architecture for predicting heat transfer and flow friction. The deviation between the predictions and experimental data was less than 4%. Compared to correlations for prediction, the performance of the ANN-based prediction exhibits ANN superiority. Then the ANN training database was expanded to include experimental data and numerical data of other similar geometries by computational fluid dynamics (CFD) for turbulent and laminar cases with the Reynolds number of 1000–10,000. This in turn indicated the prediction has a good agreement with the combined database. The satisfactory results suggest that the developed ANN model is generalized to predict the turbulent or/and laminar heat transfer and fluid flow of such three kinds of heat exchangers with large tube-diameter and large number of tube rows. Also in this paper the weights and biases corresponding to the neural network architecture are provided so that future research can be carried out. It is recommended that ANNs might be used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis.     

Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions

This paper describes an application of artificial neural networks (ANNs) to predict the thermal performance of a cooling tower under cross-wind conditions. A lab experiment on natural draft counter-flow wet cooling tower is conducted on one model tower in order to gather enough data for training and prediction. The output parameters with high correlation are measured when the cross-wind velocity, circulating water flow rate and inlet water temperature are changed, respectively. The three-layer back propagation (BP) network model which has one hidden layer is developed, and the node number in the input layer, hidden layer and output layer are 5, 6 and 3, respectively. The model adopts the improved BP algorithm, that is, the gradient descent method with momentum. This ANN model demonstrated a good statistical performance with the correlation coefficient in the range of 0.993–0.999, and the mean square error (MSE) values for the ANN training and predictions were very low relative to the experimental range. So this ANN model can be used to predict the thermal performance of cooling tower under cross-wind conditions, then providing the theoretical basis on the research of heat and mass transfer inside cooling tower under cross-wind conditions.     

基于灰色关联分析-GA-BP模型的叶绿素a含量预测

为提高水体叶绿素a预测精度和收敛速率,提出一种基于灰色关联度分析和遗传算法优化BP神经网络预测水体叶绿素a的方法。即先采用灰色关联度分析法选取合适的水质指标作为输入因子,然后优化网络隐含层的结构参数,引入遗传算法优化BP神经网络的初始权值和阈值,最后以预测太湖叶绿素a为例进行比较分析。结果表明,优化神经网络隐含层数能进一步提高网络的预测精度、缩短训练时间;灰色关联分析-GA-BP模型相较于BP、GA-BP模型具有更高的预测精度和收敛速度,可为控制水环境监测和决策平台提供科学依据。     

Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

A pattern net assisted mapping artificial neural network (PAMANN) model for estimation of parameters in problem with large data (1300 × 121 matrix size) is reported. A pattern net-based multilayer perceptron neural network (MLPNN) model for clustering the data, followed by mapping MLPNN model for mapping the target with the input, is developed as PAMANN model. A heat transfer problem with combined mode conduction and radiation in porous medium is solved numerically, and is called direct model. In the inverse model, a PAMANN model is developed by using data generated through the direct model. The PAMANN model is able to estimate two parameters (extinction coefficient β and convective coupling P₂) after taking temperature profile as input. The model is tested for different number of neurons in hidden layer, and different levels of noise in input data. Twelve different algorithms are explored in training of mapping MLPNN, and compared for performance. Levenberg–Marquardt algorithm is found to estimate the parameters with high accuracy, but took high CPU time. Bayesian regularization is found to consume very high CPU time with moderate accuracy in estimation of parameters. Variations in hidden layer neuron number and noise in input data, were done to analyze the performance of mapping MLPNN with different training algorithms. Algorithms O-Step Secant, conjugate gradient with Polak-Ribiére updates, and conjugate gradient with Fletcher-Reeves updates are able to handle all variations of noise and number of neurons in hidden layer, with good accuracy of estimation and low CPU time consumption. Under high computational resource LM algorithm can be used for all cases. Up to 0.99132 value of regression coefficient is obtained in mapping MLPNN model with 15 hidden neurons, indicating the high accuracy of the model. With the help of PAMANN model, highly accurate (absolute error 1.78%) estimation of parameters is obtained. The model can handle upto 1% noise in input data, while giving accurate results.     

Effect of direction on wind speed estimation in complex terrain using neural networks

A method of estimating the annual average wind speed at a selected site using neural networks is presented. The method proposed uses only a few measurements taken at the selected site in a short time period and data collected at nearby fixed stations.The neural network used in this study is a multilayer perceptron with one hidden layer of 15 neurons, trained by the Bayesian regularization algorithm. The number of inputs that must be used in the neural network was analyzed in detail, and results suggest that only wind speed and direction data for a single station are required. In sites of complex terrain, direction is a very important input that can cause a decrease of 23% in root mean square (RMS).The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations are satisfactory, with errors below 2%.     

Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

This paper presents an application of the neural networks for identification of the maximum power (MP) and the normal operating power (NOP) of a photovoltaic (PV) module. Two neural networks are developed; the first is the maximum power neural network (MPNN) and the second is the normal operating power neural network (NOPNN). The two neural networks receive the solar radiation and the PV module surface temperature as inputs, and estimate the MP and the NOP of a PV module as outputs. The training process for the two neural networks used a series of input/output data pairs. The training inputs are the solar radiation and the PV module surface temperature, while the outputs are the PV module MP for the MPNN and the PV module NOP for the NOPNN. The results showed that, the proposed neural networks introduced a good accurate prediction for the PV module MP and NOP compared with the measured values.     

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties.     

Intelligent modeling of rheological and thermophysical properties of nanoencapsulated PCM slurry

Nanoencapsulated phase change material slurries (NPCMS) combine properties of carried fluid and phase change material (PCM). Usage of NPCMS instead of water as a working fluid has a lot of advantages in many industrial fields. The costly and time‐consuming determination of thermophysical properties of NPCMS through the experimental analysis led the current investigations to use soft computing methods like correlating, artificial neural network (ANN), and ant colony optimization (ACO_R). In this study, the application of ANN, empirical correlations, and ACO_R for modeling the thermophysical properties of NPCM slurry, which has been synthesized through a facile and eco‐friendly procedure, has been investigated. PCM nanocapsules have been synthesized using a miniemulsion polymerization method. Nancapsules consist of AP‐25 as core and a Styrene shell, which is modified with graphene oxide nanosheets as an extra protective screen. The morphology and thermal properties of nanocapsules were characterized and analyzed, respectively. Results revealed that minimum average particle‐size values result in a melting latent heat of 146.8 J/g. In case of NPCM slurry, the results showed that the thermal conductivity of MPCS decreased with particle concentration for the temperatures below the melting point. The NPCMS can be considered a Newtonian fluid within the test region (shear rate > 200/seconds and mass fraction < 0.25). The ANN‐ACO_R model consists of two neurons in the input layer, six neurons in the hidden layer, and two neurons in the output layer. The input layer consists of two nodes (PCM concentration and temperature) that correspond to parameters found essential and sufficient for thermophysical properties prediction. Upon comparison, the results show that the presented model, which is a combination of the ACO_R algorithm and an artificial neural network, is compatible with experimental work.     

Neural network approach for food temperature prediction during solar drying

In the present study, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated. The important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling. Experimental data on potato cylinders and slices obtained with mixed mode solar dryer for 9 typical days of different months of the year were used for training and testing the neural network. A methodology is proposed for development of optimal neural network. Results of analysis reveal that the network with 4 neurons and logsig transfer function and trainrp back propagation algorithm is the most appropriate approach for both potato cylinders and slices based on minimum measures of error. In order to test the worthiness of ANN model for prediction of food temperature variation, the analytical heat diffusion model with appropriate boundary conditions and statistical model are also proposed. Based on error analysis results, the prediction capability of ANN model is found to be the best of all the prediction models investigated, irrespective of food sample geometry.     

Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha^?1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R²) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.