Predicting Nanofiltration Performance during Treatment of Welding Electrode Manufacturing Wastewater, Using Artificial Neural Networks

2014 ◽  
Vol 618 ◽  
pp. 55-59
Author(s):  
H. Alizadeh Golestani
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Cross Flow ◽  
Transmembrane Pressure ◽  
Permeate Flux ◽  
Absolute Deviation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
Welding Electrode

This paper presents artificial neural network (ANN) predictions for a nanofiltration membrane used to treat wastewater of welding electrode manufacturing in a cross flow set up. The main parameters were time, feed flow rate, and transmembrane pressure (TMP). The experimental data were correlated and analyzed using ANN. ANN’s prediction of the permeate flux, turbidity, total dissolved solids (TDS), hardness for various TMPs, and flow rates are discussed. The effects of the training algorithm, neural network architectures, and transfer function on the ANN performance, as reflected by the percentage average absolute deviation, are discussed. A network with one input layer, 50-100 hidden layers, and one output layer is found to be adequate for mapping input–output relationships and providing a good interpolative tool. A good agreement has been obtained between the ANN predictions and the experimental data with a deviation below 2% for all cases considered.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

A Prediction Model of Deflection of Fixing Thin Parts

2012 ◽  
Vol 217-219 ◽  
pp. 1526-1529
Author(s):  
Yu Mei Liu ◽  
Wen Ping Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Simulated Data ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

scholarly journals Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

Membranes ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 372
Author(s):  
Aleksandar Jokić ◽  
Ivana Pajčin ◽  
Jovana Grahovac ◽  
Nataša Lukić ◽  
Bojana Ikonić ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Flow ◽  
Process Efficiency ◽  
Air Sparging ◽  
Permeate Flux ◽  
Bacillus Velezensis ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Cultivation Broth

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg–Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.

scholarly journals Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

2015 ◽  
Vol 35 (2) ◽  
pp. 266-279 ◽  
Author(s):  
ODÍLIO C. DA ROCHA NETO ◽  
ADUNIAS DOS S. TEIXEIRA ◽  
ARTHUR P. DE S. BRAGA ◽  
CLEMILSON C. DOS SANTOS ◽  
RAIMUNDO A. DE O. LEÃO
Keyword(s):  
Neural Network ◽  
Water Balance ◽  
Irrigation Management ◽  
Economic Interest ◽  
Irrigation Strategy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
Computational Resources ◽  
Precision Irrigation

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

scholarly journals Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmad Azari ◽  
Saeid Atashrouz ◽  
Hamed Mirshekar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Vapor Liquid Equilibria ◽  
Binary Refrigerant ◽  
Binary Refrigerant Mixtures ◽  
Vapor Liquid

Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary refrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane (R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-tetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore, some new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results confirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is capable of predicting the azeotropic condition.

Inverse Analysis of Material Parameters of Multiple Foam Layers Based on Artificial Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3313-3316
Author(s):  
Xiao Xiang Su ◽  
Yao Dong Gu ◽  
J.P. Finlay ◽  
I.D. Jenkinson ◽  
Xue Jun Ren
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Nonlinear Material ◽  
Fe Model ◽  
Ann Model ◽  
Nonlinear Parameters ◽  
Closed Cell ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Closed cell polymeric foams are widely used in sport and medical equipments. In this study, an artificial neural network (ANN) based inverse finite element (FE) program has been developed and used to predict the nonlinear material properties of EVA foams with multiple layers. A 2-D parametric FE model was developed and validated against experimental data. Systematic data from FE simulations was used to train and validate the ANN model. The accuracy and validity of the ANN method were assessed based on both blind tests and experimental data. Results showed that the proposed artificial neural network model is robust and efficient in predicating the nonlinear parameters of foam materials.

scholarly journals Experimental and Artificial Neural Network (Ann) Simulation of a Solar Cavity Collector

2019 ◽  
Vol 8 (6) ◽  
pp. 2163-2170
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Water Mass ◽  
Outer Radius ◽  
Water Pipes ◽  
Mass Flow Rates ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Flat Plate Solar Collector

Solar cavity collector (SCC) is an improvised version of flat plate solar collector (FPC). A SCC of outer radius 16mm positioned concentrically and placed in a 50 mm metal box. Five numbers of such cavities with a provision of inlet and outlet water pipes has been fabricated and experimented for its optimal performance. This experimental gadget is used to heat the water. As the physical dimensions of solar cavity collector influence the performances of the cavity collector, it includes the comparison of 5 numbers of cavities and 7 numbers of cavities, effect of aperture entry have been taken as investigation parameters in the present study. Inclination angle of the collector, water mass flow rates and mode of flow are the other parameters taken for the present study. Experimental data are trained and tested using Artificial Neural Network (ANN) tool of MATLAB software and ANN simulation results have been validated and verified with the available experimental data. Simulations for other set of variables have been predicted with the developed ANN mode

Prediction of Springback of the One-Axle Rotary Shaping Based on Artificial Neural Network

2013 ◽  
Vol 535-536 ◽  
pp. 318-321
Author(s):  
Xia Jin ◽  
Shi Hong Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Software ◽  
Forming Process ◽  
Training Samples ◽  
Metal Forming Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
The One

One-axle rotary shaping with the elastic medium (RSEM) is a kind of advanced sheet metal forming process. The research object is the springback of aluminous U-section. The orthogonal method is used to arrange the simulation experiments, the forming and springback of the workpiece are simulated successfully with the Finite Element Simulation software, and The main factors influenced the RSEM are analyzed. The simulation results are used as the training samples of the artificial neural network (ANN), and the ANN prediction model of RSEM process is set up. The prediction results would be tested with the experiment data, and only a little tolerance was existed between the two values. It demonstrated that the combination of orthogonal test, numerical simulation and neural network could effectively predict the springback of RSEM, the design efficiency of process parameters would be improved. It would guide the development of precision forming technology.

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