scholarly journals Prediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset

2021 ◽  
Author(s):  
Jabar Yousif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Neural Model ◽  
Ann Model ◽  
Pv System ◽  
Experimental Dataset ◽  
Ann Models ◽  
Artificial Neural ◽  
T System

The current work aims to predict and assess a PV/T system using <a href="https://www.sciencedirect.com/topics/engineering/artificial-neural-network-model">ANN models</a> based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent <a href="https://www.sciencedirect.com/topics/engineering/mean-squared-error">MSE</a> results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed <a href="https://www.sciencedirect.com/topics/engineering/neural-model">neural models</a> can generate future figures for any needed period that accurately fit the actual datasets.

scholarly journals Prediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset

2021 ◽  
Author(s):  
Jabar Yousif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Neural Model ◽  
Ann Model ◽  
Pv System ◽  
Experimental Dataset ◽  
Ann Models ◽  
Artificial Neural ◽  
T System

The current work aims to predict and assess a PV/T system using <a href="https://www.sciencedirect.com/topics/engineering/artificial-neural-network-model">ANN models</a> based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent <a href="https://www.sciencedirect.com/topics/engineering/mean-squared-error">MSE</a> results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed <a href="https://www.sciencedirect.com/topics/engineering/neural-model">neural models</a> can generate future figures for any needed period that accurately fit the actual datasets.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

scholarly journals Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

2014 ◽  
Vol 7 (4) ◽  
pp. 132-143
Author(s):  
ABBAS M. ABD ◽  
SAAD SH. SAMMEN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Water Reservoir ◽  
Prediction Method ◽  
Training Data ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Poonpat Poonnoy ◽  
Ampawan Tansakul ◽  
Manjeet Chinnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Ratio ◽  
Vacuum Pressure ◽  
Ann Model ◽  
Drying Time ◽  
Feed Forward ◽  
Marquardt Algorithm ◽  
Ann Models ◽  
Artificial Neural

The drying rate of a mushroom undergoing microwave-vacuum (MV) drying (MVD) was controlled by moisture dissipation and was dependent on vacuum pressure levels. The main objective of this work was to develop artificial neural network (ANN) model to predict moisture ratio of MV-dried mushrooms. One-hidden-layer feed-forward ANN models were trained and validated with experimental data. The Levenberg-Marquardt algorithm was utilized in regulating the ANN model weights and biases. Inputs for ANN models were vacuum pressure and drying time. Output from ANN models was moisture ratio at a given drying time. Reduced chi-square (X 2) and root mean square error (RMSE), and residual sum of squares (RSS) of the results from ANN models were calculated and compared with those of a modified Page's model (an experimental-based mathematical model), which is commonly used in the literature. The X 2, RMSE, and RSS of the ANN model (2.272 x 10 -5, 4.023 x 10 -3, and 3.204 x 10 -3, respectively) were found to be lower than those of the modified Page's model (6.692 x 10 -4, 2.561 x 10 -2, and 12.98 x 10 -2, respectively). These results indicate that the feed-forward ANN model represented the drying characteristics of mushrooms better than the modified Page's model. Therefore, the ANN model could be considered as a better tool for estimation of the moisture content of mushrooms than by the modified Page's model.

scholarly journals Study of correlation between the steels susceptibility to hydrogen embrittlement and hydrogen thermal desorption spectroscopy using artificial neural network

2020 ◽  
Vol 32 (18) ◽  
pp. 14995-15006 ◽  
Author(s):  
Evgenii Malitckii ◽  
Eric Fangnon ◽  
Pedro Vilaça
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Embrittlement ◽  
Thermal Desorption ◽  
Thermal Desorption Spectroscopy ◽  
Coupled System ◽  
Computing System ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural

Abstract Steels are the most used structural material in the world, and hydrogen content and localization within the microstructure play an important role in its properties, namely inducing some level of embrittlement. The characterization of the steels susceptibility to hydrogen embrittlement (HE) is a complex task requiring always a broad and multidisciplinary approach. The target of the present work is to introduce the artificial neural network (ANN) computing system to predict the hydrogen-induced mechanical properties degradation using the hydrogen thermal desorption spectroscopy (TDS) data of the studied steel. Hydrogen sensitivity parameter (HSP) calculated from the reduction of elongation to fracture caused by hydrogen was linked to the corresponding hydrogen thermal desorption spectra measured for austenitic, ferritic, and ferritic-martensitic steel grades. Correlation between the TDS input data and HSP output data was studied using two ANN models. A correlation of 98% was obtained between the experimentally measured HSP values and HSP values predicted using the developed densely connected layers ANN model. The performance of the developed ANN models is good even for never-before-seen steels. The ANN-coupled system based on the TDS is a powerful tool in steels characterization especially in the analysis of the steels susceptibility to HE.

scholarly journals Decision support system for tool condition monitoring in milling process using artificial neural network

2021 ◽  
Author(s):  
.Mohanraj T ◽  
◽  
Tamilvanan A. ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Mean Squared Error ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Vibration Signals ◽  
Tool Condition ◽  
Artificial Neural

This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

scholarly journals Live to learn: learning rules-based artificial neural network

2021 ◽  
Vol 21 (1) ◽  
pp. 558
Author(s):  
Aseel Shakir I. Hilaiwah ◽  
Hanan Abed Alwally Abed Allah ◽  
Basim Akhudir Abbas ◽  
Tole Sutikno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Output Layer ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Intelligent Model ◽  
Human Nervous System

<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

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