scholarly journals Liquid Density Prediction of Ethanol/Water, Using Artificial Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 5625-5637
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Functions ◽  
Liquid Density ◽  
Ann Model ◽  
Average Absolute Deviation ◽  
Absolute Deviation ◽  
Marquardt Algorithm ◽  
Reliable Model ◽  
Artificial Neural

In this work, our objective was to get a reliable model for predicting liquid density ethanol-water and use it again later in modeling the ethanol production process from biomass. Hence, the unreliability of the Peng-Robinson equation of state to predict this property was shown. The average absolute deviation of this prediction is equal to 14.72 %. To have a reliable model, an artificial neural network (ANN) method was followed. Levenberg–Marquardt algorithm is used to choose the optimized ANN structure that has ten neurons in the hidden layer, three neurons in the input layer, and one neuron in the output layer, with a tangent-sigmoid and linear transfer functions, in the hidden and the output layers, respectively. The model training was done using 348 experimental data points from published experiments, realized at different liquid mole fraction range, pressure (0.10 to 10.00MP), and temperature (298.15 K to 476.2 K). The correlation coefficient between the experimental and liquid phase density was 0.9999 for training, validation, and testing the model. Statistical analysis is employed to evaluate the accuracy of the ANN, showing that the average absolute deviation, root mean square, and the Bias are 0.047 %, 0.003 %, and -0.004 %, respectively. So the ANN model gives a good estimation of liquid density, for mixture ethanol/water, with a relative importance of pressure, composition, and temperature equal to 41%, 34 %, and 25 %, respectively.

scholarly journals Artificial Neural Network (ANN) Modeling for Prediction of Pesticide Wastewater Degradation by FeGAC/H2O2 Process

2018 ◽  
Vol 65 ◽  
pp. 05004
Author(s):  
Augustine Chioma Affam ◽  
Malay Chaudhuri ◽  
Chee Chung Wong ◽  
Chee Swee Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Close Agreement ◽  
Transfer Functions ◽  
Cod Removal ◽  
Experimental Results ◽  
Ann Model ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

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 An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

2015 ◽  
Vol 27 (3) ◽  
pp. 217-225 ◽  
Author(s):  
Muhammed Yasin Çodur ◽  
Ahmet Tortum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Traffic Accidents ◽  
Model Parameters ◽  
Mean Square ◽  
Ann Model ◽  
Accident Prediction ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

scholarly journals Biomedical Prediction of Radial Size of Powdered Element using Artificial Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1593-1602
Author(s):  
Yaagyanika Gehlot ◽  
Bhairvi Sharma ◽  
P. Muthu ◽  
Hariharan Muthusamy ◽  
S. Latha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Functions ◽  
Curcuma Longa ◽  
Absolute Error ◽  
Ann Model ◽  
Ag Nps ◽  
Optimum Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Silver nitrous aqueous solution is used to biosynthesize Silver nanoparticles (Ag-NPs) through a green and easy way using tuber powder extracts of Curcuma Longa (C. longa). The aim is to model an Artificial Neural Network (ANN) using seven existing algorithms in MATLAB for forecasting the size of the silver nanoparticle with volume of both C. longa extraction and AgNO3, time of stirring and temperature of reaction as input functions. Several techniques including Quasi-Newton, Conjugate Gradient and Levenberg-Maquardt are employed for training the designed ANN model, a feed-forward backpropagation network with different combinations of architecture and transfer functions. Each algorithm is fashioned to obtain the best performance by calculating the Regression (R), Mean Square Error (MSE), Mean Absolute Error (MAE) and Error Sum of Squares (SSE), thereby comparing the results and propounding the optimum algorithm technique for the discussed application in nanoengineering. Finally, based on the findings, the optimum network is proposed through the simulation results.

scholarly journals Simulation of Dye Synthesized Solar Cell using Artificial Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 1316-1322
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Cell ◽  
Transfer Function ◽  
Short Circuit ◽  
Ann Model ◽  
Minimum Mean Squared Error ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
The Impact

The primary goal of present examination is to foresee every day worldwide solar cell efficiency in view of meteorological factors, utilizing distinctive counterfeit neural system (ANN) procedures. In the present examination we report the impact of Dye Synthesized solar cell. A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Dye Synthesized solar cell based on 100 experimental sets. In the present examination we report the impact of Dye Synthesized solar cell. The effect of operational parameters such as short circuit current (Jsc),Open circuit voltage(Voc),Fill factor(FF) were studied to optimize the conditions to check the efficiency of Dye Synthesized solar cell. Experimental results showed that the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 20 neurons and a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm (LMA) was used with a minimum mean squared error (MSE) of 0.00350141. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.9993 for six model variables used in this study

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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 Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

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