Artificial neural network modeling and optimization of desalination by air gap membrane distillation

Khayet, M.; Cojocaru, Corneliu

doi:10.1016/j.seppur.2011.11.001

Cited by 129 publications

(60 citation statements)

References 27 publications

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“…To avoid overfitting, both input variables and response were normalized before training. The input variables were normalized so that they can vary in the range [0-1] according to the following relationship [13]:…”

Section: Resultsmentioning

confidence: 99%

“…The most employed performance function is the mean-squared-error (MSE). In the case of a single response (output neuron), MSE may be written as [13,21]:…”

Section: Ann Theoreticalmentioning

confidence: 99%

“…Recently we have reported on the development and application of an ANN model to predict the AGMD desalination performance index [13]. The ANN model was used for optimization of the AGMD process and the following optimal conditions were obtained, an air gap thickness of 3.0 mm, a condensation temperature of 13.9°C, a feed inlet temperature of 71°C and a feed flow rate of 205 L/h.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, external condensers are required to collect the vapor in the permeate side stream, complicating in this way the system design and increasing its cost. Similar to the other MD configurations, the feed liquid solution is maintained at a higher temperature than the temperature in the permeate side [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…The most employed performance function is the mean-squared-error (MSE). In the case of a single response (output neuron), MSE may be written as [13,21]:…”

Section: Ann Theoreticalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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“…The optimization based on Monte Carlo   0,1 Dist U  methods can be useful for solving optimization problems with many local optima and complicated constraints, possibly involving a mix of continuous and discrete variables (Kroese et al, 2011). When using the MC method for solving optimization problems there are basically two approaches: single and multi-stage (Dhavlikar et al 2003;Khayet & Cojocaru 2012). In a multi-stage approach, simulation runs are repeated by modifying the bounds of each independent variable considering the near optimum solution obtained in the previous simulation run, which is illustrated in Fig.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

Application of multi-stage Monte Carlo method for solving machining optimization problems

Madić¹,

Kovačević²,

Radovanović³

2014

10.5267/j.ijiec

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Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.

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Experimental Study and Artificial Intelligence Modeling of Dye Removal in Microfluidic Systems

Hosseini

Rahimi²

2023

Chem Eng & Technol

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A modeling approach was utilized to achieve efficient operational conditions for Alizarin removal from synthetic wastewater in a T‐type micromixer. Besides experimental work, the neuro‐fuzzy system and artificial neural network techniques were utilized for this purpose. Input parameters were the pH, the initial Alizarin concentration in the feed, the extractant volume percentage in the organic phase, and the fluid flow rate. Based on the obtained results, both models have high precision. However, the accuracy of the neural network for estimating the extraction percentage is higher compared to that of the neuro‐fuzzy model. The optimal values of the operating parameters were determined by the genetic algorithm technique and the extraction percentage value was obtained as about 99.4 %.

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Artificial neural network modeling and optimization of desalination by air gap membrane distillation

Cited by 129 publications

References 27 publications

Artificial neural network model for desalination by sweeping gas membrane distillation

Artificial neural network model for desalination by sweeping gas membrane distillation

Application of multi-stage Monte Carlo method for solving machining optimization problems

Experimental Study and Artificial Intelligence Modeling of Dye Removal in Microfluidic Systems

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