Artificial neural network modeling and response surface methodology of desalination by reverse osmosis

Khayet, M.; Cojocaru, Corneliu; Essalhi, M.

doi:10.1016/j.memsci.2010.11.030

Cited by 202 publications

(101 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most important phase for building ANN model is the training of the network. During the training process the weights and biases of a feed-forward neural network are adjusted systematically in order to minimize the residual error between network outputs (predictions) and targets (experimental data) [11,23,24]. There is a variety of training algorithms.…”

Section: Ann Theoreticalmentioning

confidence: 99%

“…There is a variety of training algorithms. The most used classes of training methods for feed-forward neural networks are the back-propagation (BP) algorithms [11,17,18,23,24]. Training of ANN using BP algorithm is an iterative optimization process applied for performance function minimization by adjusting the network weights and biases appropriately.…”

Section: Ann Theoreticalmentioning

confidence: 99%

“…MSE), η is the learning rate and k is the integer index indicating the epoch (iteration) in learning phase. Once the ANN was trained the optimal weights and biases are saved and the neural network model can be used for simulation and optimization [11,23]. …”

Section: Ann Theoreticalmentioning

confidence: 99%

“…In our previous paper [11] we compared the ANN model with response surface model (RSM) in terms of prediction and optimization of desalination by RO process. The ANN model was found to be more adequate in prediction of the RO performance index than the RSM empirical model in a wide range of salt concentrations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

Self Cite

View full text Add to dashboard Cite

Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69°C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493×10−3 kg/m 2 .s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

show abstract

Section: Ann Theoreticalmentioning

confidence: 99%

Section: Ann Theoreticalmentioning

confidence: 99%

Section: Ann Theoreticalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

Self Cite

View full text Add to dashboard Cite

show abstract

“…ANN is a modeling tool to solve linear and nonlinear multivariate regression problems. 22 Recently, ANN models were widely utilized to interpret and correlate the variable relationships in complex nonlinear data sets. The present study proposes a new approach based on ANNs to investigate the effects of SP process on mechanical and metallurgical properties (TiB + TiC)/Ti-6Al-4V composite.…”

Section: Introductionmentioning

confidence: 99%

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Maleki¹,

Zabihollah²

2016

Mater. Tehnol.

View full text Add to dashboard Cite

Titanium matrix composites (TMCs) have wide application prospects in the field of aerospace, automobile and other industries because of their good properties, such as high specific strength, good ductility, and excellent fatigue properties. However, in order to improve their fatigue strength and life, crack initiation and growth at the surface layers must be suppressed using surface treatments. Shot peening (SP) is an effective surface mechanical treatment method widely used in industry which can improve the mechanical properties of a surface. However, artificial neural networks (ANNs) have been used as an efficient approach to predict and optimize the science and engineering problems. In the present study the effects of SP on TMC were modeled by means of ANN and the capability of the ANN in predicting the output parameters is investigated. A back-propagation (BP) error algorithm is developed for the network training. Data of experimental tests on the (TiB + TiC)/Ti-6Al-4V composite are employed in order to train the network. The volume fractions of the reinforcements (TiB + TiC) were 5 % and 8 %. ANN testing is accomplished using different experimental data thaat were not used during the network training. The distance from the surface (depth) and SP intensity are regarded as input parameters and residual stress and hardness of the Ti-6Al-4V before and after the SP and adding reinforcements are gathered as the output parameters of the network. A comparison was made between experimental and predicted data. The predicted results were in good agreement with experimental ones, which indicates that developed neural network can be used for modeling the SP process on TMCs. Keywords: titanium matrix composites, surface treatment, shot peening, artificial neural networks, residual stress, hardness Kompoziti na osnovi titana (TMCs) imajo {iroko mo`nost uporabe na podro~ju letalstva, avtomobilske in druge industrije zaradi njihovih dobrih lastnosti, kot so: velika specifi~na trdnost, dobra duktilnost in odli~na odpornost na utrujanje. Vseeno pa je za pove~anje odpornosti na utrujanje in`ivljenjsko dobo, potrebna povr{inska obdelava, da se zavre nastanek razpok in njihova rast na povr{ini. Hladno povr{insko kovanje (SP) je u~inkovita mehanska metoda, ki se v industriji pogosto uporablja za izbolj{anje mehanskih lastnosti povr{ine. Umetne nevronske mre`e (ANNs) se uporabljajo kot u~inkovit pribli`ek za napovedovanje in optimiranje znanstvenih osnov in in`eniringa tega problema. V {tudiji so bili modelirani vplivi SP na TMC s pomo~jo ANN in preiskovana je bila zmo`nost napovedovanja izhodnih parametrov z ANN. Za usposabljanje mre`e je bil razvit algoritem vzvratnega {irjenja napak (BP). Podatki iz eksperimentalnih preizkusov na (TiB + TiC)/Ti-6Al-4V kompozitu so uporabljeni za usposabljanje mre`e. Volumska dele`a delcev (TiB + TiC) za oja~anje sta bila 5 % in 8 %. ANN preizku{anje je bilo izvedeno z uporabo razli~nih eksperimentalnih podatkov, ki niso bili uporabljeni pri usposabljanju mre`e. Razdalja od povr{ine (globina) in...

show abstract

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Agarwal

Rathore

Hadpe

et al. 2016

Biotechnology Progress

View full text Add to dashboard Cite

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.

show abstract

Artificial neural network modeling and response surface methodology of desalination by reverse osmosis

Cited by 202 publications

References 40 publications

Artificial neural network model for desalination by sweeping gas membrane distillation

Artificial neural network model for desalination by sweeping gas membrane distillation

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Contact Info

Product

Resources

About