An approach based on neural networks for estimation and generalization of crossflow filtration processes

Silva, Ivan Nunes da; Flauzino, Rogério Andrade

doi:10.1016/j.asoc.2007.03.008

Cited by 31 publications

(16 citation statements)

References 9 publications

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“…In the context of flows within porous media ANN's have been used for a variety of applications that include, e.g., prediction of gas diffusion layer properties within polymer electrolyte membrane (PEM) fuel cells (Lobato et al, 2010;Kumbur et al, 2008), prediction of dialysis performance in ultrafiltration (Godini et al, 2010), hygrothermal property characterization in porous soils (Coelho et al, 2009), oil saturation and petrophysical property predictions in oilfield sands (Boadu 2001), groundwater contamination and pollutant infiltration forecasting (Tabach et al, 2007), simulating cross-flow filtration processes (Silva and Flauzino, 2008), optimization of groundwater remediation problems (Johnson and Rogers, 2000;Rogers and Dowla, 1994), large-scale water resource management (Yan and Minsker, 2006), permeability modeling in petroleum reservoir management (Karimpouli et al, 2010), water/wastewater treatment using various homogeneous and heterogeneous nano-catalytic processes (Khataee and Kasiri, 2010), determination of stress-strain characteristics in composites (Lefik et al, 2009) and characterization of outflow parameters influencing fractured aquifers outflows (Lallahem and Mania, 2003). For example, Rogers and Dowla (1994) proposed an ANN-based groundwater management model for optimizing aquifer remediation.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

Hanspal

Allison

Deka

et al. 2012

Journal of Hydroinformatics

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The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

Hanspal

Allison

Deka

et al. 2012

Journal of Hydroinformatics

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“…Most of them use the gradient descent method for iterative updating of weights and biases until the convergence is satisfied. Normally, the gradient descent updating of the network weights (w ij ) and biases (b i ) using a single iteration of BP algorithm can be written as [25]:…”

Section: Ann Theoreticalmentioning

confidence: 99%

Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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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.

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“…The main advantages of using ANNs are: it requires basic level programming and it is becoming widely accepted to simplify programming and algorithm design for a given wide range of outputs [28,36,44]. ANNs are particularly useful for solving problems that cannot be expressed as a series of steps, such as series prediction and data mining [31,45].…”

Section: Advantages and Disadvantages Of Annsmentioning

confidence: 99%

“…Thus, it is a very useful modelling tool with neurons operating in parallel, typically in three layers; input, hidden and output [27][28][29][30][31]. An ANN can be trained to perform a particular function by adjusting the values of the weights assigned to the neurons between the inputs and the output [32][33][34].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Mahdi

Holdich

2016

Separation Science and Technology

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Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (MVR) and Artificial Neural Network (ANN) methods utilized additional particle characteristics ('fines ratio' (x 50 /x 10 ) and particle shape) that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny-Carman and HappelBrenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques.

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An approach based on neural networks for estimation and generalization of crossflow filtration processes

Cited by 31 publications

References 9 publications

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

Artificial neural network model for desalination by sweeping gas membrane distillation

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

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