scholarly journals Multilayered artificial neural network for performance prediction of an adiabatic solar liquid desiccant dehumidifier

2019 ◽  
Vol 14 (3) ◽  
pp. 351-363
Author(s):  
Andrew Y A Oyieke ◽  
Freddie L Inambao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vapour Pressure ◽  
Air Conditioning ◽  
Ann Model ◽  
Liquid Desiccant ◽  
Humidity Ratio ◽  
Packed Tower ◽  
The Mean ◽  
Artificial Neural

Abstract In this study, a multi-layered artificial neural network (ANN) algorithm was developed and trained to predict the performance of a solar powered liquid desiccant air conditioning (LDAC) system particularly the adiabatic packed tower dehumidifier using Lithium Bromide (LiBr) desiccant. A reinforced technique of supervised learning based on error correction principle rule coupled with perceptron convergence theorem was applied to create the algorithm. The parameters such as temperature, flow rates and humidity ratio of both air and desiccant fluid were fed as inputs to the ANN algorithm and their respective outputs used to determine dehumidifier effectiveness and moisture removal rate (MRR). The ANN model when subjected to validity tests using vapour pressure of LiBr desiccant solution at specific random temperatures and concentrations, gave astounding outcomes with precise estimation to R2 values of 0.9999 for all desiccant concentration levels. Due to the variation in solar radiation, the MRR and effectiveness fluctuated with the change in desiccant and air temperatures, giving maximum differences of 0.2 g/s and 1.8% respectively between the predicted and measured values depicting a perfect match. With respect to humidity ratio, MRR was accurately predicted by ANN algorithm with maximum difference of 3.4969% while the mean variation was −0.5957%. With respect to air temperature, the dehumidifier effectiveness was perfectly predicted by the ANN algorithm to an average accuracy of 0.53% and extreme positive deviation of 4.14%. The MRR was replicated to a mean variation of 0.013% and highest point difference of 0.08%. In all the above cases, the mean and maximum differences between the ANN model and experimental values were far below the allowable limit of ± 5%, hence the algorithm was deemed to be successful and could find use in air conditioning scenarios. The ANN algorithm’s capability and flexibility test of processing unforeseen inputs was accurate with negligible deviations and prospects of predicting the desiccant’s vapour pressure, dehumidifier effectiveness and MRR within all ranges of temperature and concentration which then eliminates the need for use of charts.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

2021 ◽  
Author(s):  
Geoffroy Chaussonnet ◽  
Sebastian Gepperth ◽  
Simon Holz ◽  
Rainer Koch ◽  
Hans-Jörg Bauer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Space ◽  
Middle Layer ◽  
Ann Model ◽  
Deformation Velocity ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Domain Of Parameters

Abstract A fully connected Artificial Neural Network (ANN) is used to predict the mean spray characteristics of prefilming airblast atomization. The model is trained from the planar prefilmer experiment from the PhD thesis of Gepperth (2020). The output of the ANN model are the Sauter Mean Diameter, the mean droplet axial velocity, the mean ligament length and the mean ligament deformation velocity. The training database contains 322 different operating points. Two types of model input quantities are investigated and compared. First, nine dimensional parameters are used as inputs for the model. Second, nine non-dimensional groups commonly used for liquid atomization are derived from the first set of inputs. The best architecture is determined after testing over 10000 randomly drawn ANN architectures, with up to 10 layers and up to 128 neurons per layer. The striking results is that for both types of model, the best architectures consist of only 3 hidden layer in the shape of a diabolo. This shape recalls the shape of an autoencoder, where the middle layer would be the feature space of reduced dimensionality. It was found that the model with dimensional input quantities always shows a lower test and validation errors than the one with non-dimensional input quantities. In general, the two types of models provide comparable accuracy, better than typical correlations of SMD and droplet velocity. Finally the extrapolation capability of the models was assessed by a training them on a confined domain of parameters and testing them outside this domain.

Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

10.17158/320 ◽  
2014 ◽  
Vol 18 (2) ◽  
Author(s):  
Eric John G. Emberda ◽  
Den Ryan L. Dumas ◽  
Timothy Pierce M. Rentillo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Forecast Data ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.</p>

Finding the Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

2020 ◽  
Author(s):  
G. Chaussonnet ◽  
S. Gepperth ◽  
S. Holz ◽  
R. Koch ◽  
H.-J. Bauer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Ambient Pressure ◽  
Middle Layer ◽  
Operating Conditions ◽  
Ann Model ◽  
Deformation Velocity ◽  
The Mean ◽  
Artificial Neural

Abstract A fully connected Artificial Neural Network (ANN) is used to predict the spray characteristics of prefilming airblast atomization. The model is trained from the planar prefilmer experiment from the PhD thesis of Gepperth [Experimentelle Untersuchung des Primärzerfalls an generischen luftgestützten Zerstäubern unter Hochdruckbedingungen, Vol. 75. Logos Verlag Berlin GmbH], in which shadowgraphy images of the liquid breakup at the atomizing edge capture the characteristics of the primary droplets and the ligaments. The quantities extracted from the images are the Sauter Mean Diameter, the mean droplet axial velocity, the mean ligament length and the mean ligament deformation velocity. These are the prescribed output of the ANN model. In total, the training database contains 322 different operating points at which different prefilmers, liquid types, ambient pressures, film loadings and gas velocities were investigated. Two types of model input quantities are investigated. First, nine dimensional parameters related to the geometry, the operating conditions and the properties of the liquid are used as inputs for the model. Second, nine non-dimensional groups commonly used for liquid atomization are derived from the first set of inputs. These two types of inputs are compared. The architecture providing the best fitting is determined after testing over 10000 randomly drawn ANN architectures, with up to 10 layers and up to 128 neurons per layer. The striking results is that for both types of model, the best architectures consist of a shallow net with the hidden layers in the form of a diabolo: three layers with a large number of neurons (≥ 64) in the first and the last layer, and very few neurons (≈12) in middle layer. This shape recalls the shape of an autoencoder, where the middle layer would be the feature space of reduced dimensionality. The trend highlighted by our results, to have a limited number of layers, is in contrast with recent observations in Deep Learning applied to computer vision and speech recognition. It was found that the model with dimensional input quantities always shows a lower test and validation errors than the one with non-dimensional input quantities. The best architectures for both types of inputs (dimensional and non-dimensional input) were tested versus the experiments. Both provide comparable accuracy, which is better than typical correlations of SMD and droplet velocity. As the models takes more input parameters into account compared to the correlations, they can predict the experimental data more accurately. Finally the extrapolation capability of the models was assessed by a training them on a confined domain of parameters and testing them outside this domain. It was found that the models can extrapolate at larger gas velocity. With a larger ambient pressure or a lower trailing edge thickness, the accuracy decreases drastically.

Use of artificial neural network in performance prediction of Solid desiccant powered Vapor compression air conditioning systems

2021 ◽  
pp. 05-19
Author(s):  
Jani DB
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Experimental Test ◽  
Air Conditioning ◽  
Coefficient Of Performance ◽  
Vapor Compression ◽  
Ann Model ◽  
Cooling Period ◽  
Artificial Neural

In the present study, artificial neural network (ANN) model for a solid desiccant-vapor compression hybrid air-conditioning system is developed to predict the cooling capacity, power input and coefficient of performance (COP) of the system. This paper also describes the experimental test set up for collecting the required experimental test data. The experimental measurements are taken at steady state conditions while varying the input parameters like air stream flow rates and regeneration temperature. Most of the experimental test data (80%) are used for training the ANN model while remaining (20%) are used for the testing of ANN model. Experimental data were collected during cooling period of March to September. The outputs predicted from the ANN model have a high coefficient of correlation (R>0.988) in predicting the system performance. The results show that the ANN model can be applied successfully and can provide high accuracy and reliability for predicting the performance of the hybrid desiccant cooling systems. Keywords: Artificial neural network; Coefficient of performance; Dehumidifier effectiveness; Moisture removal rate, TRNSYS

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.

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