Modeling and simulation of VMD desalination process by ANN

Cao, Wensheng; Liu, Qiang; Wang, Yongqing; Mujtaba, Iqbal M.

doi:10.1016/j.compchemeng.2015.08.019

Cited by 66 publications

(15 citation statements)

References 18 publications

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“…ANNs are efficiently exploited for modeling various problems due to their ability to model linear and nonlinear systems without the need to make assumptions implicitly as in most conventional statistical approaches. Currently, this artificial intelligence approach is extensively applied in various aspects of sciences, engineering, and technology: agricultural sciences, [26][27][28] medicine, 29,30 business, management and accounting, 31,32 energy, [33][34][35] environmental sciences, 36,37 chemical engineering, 38,39 engineering, [40][41][42][43] and so one.…”

Section: Methodsmentioning

confidence: 99%

Modeling of cutting performances in turning process using artificial neural networks

Dahbi

Ezzine

Moussami

2017

International Journal of Engineering Business Management

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In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient (R 2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R 2 > 99%, MSE < 0.3%, and APE < 6%.

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Section: Methodsmentioning

confidence: 99%

Modeling of cutting performances in turning process using artificial neural networks

Dahbi

Ezzine

Moussami

2017

International Journal of Engineering Business Management

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“…2 summarizes the proposals made up to now in the literature for modeling MD systems by means of ANN based models. As can be seen, almost all the works use ANN for characterizing only the permeate production of a MD unit [10,11,14,27,28], and only Shibazian and Alibabaei [29] consider also the thermal energy. Furthermore, the feed water salt concentration is only considered by Cao et al [27] and Tavakolmoghadam and Safavi [28].…”

Section: Introductionmentioning

confidence: 99%

Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants

et al. 2018

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Desalting brines from Reverse Osmosis (RO) plants is one of the most promising applications of Membrane Distillation (MD) systems. The development of accurate models to predict MD system performances plays a significant role in the design of this kind of industrial applications. In this paper, a commercialscale Permeate Gap Membrane Distillation (PGMD) module was modeled by means of two different approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Condenser inlet temperature, evaporator inlet temperature, feed flow rate and feed water salt concentration were selected as inputs of the model, while permeate flux and Specific Thermal Energy Consumption (STEC) were chosen as responses. The prediction abilities of both RSM and ANN models were compared with further experimental data by using the Analysis of Variance (ANOVA) and the Root Mean Squared Error (RMSE). The results show that the ANN model is able to predict in a more precise way the behaviour of the module for the whole range of input variables. Thus, ANN model was used to find the optimal operating conditions, for the module operating at feed water salinity of 70 and 105 g/L, concentrations that can be reached when desalting RO brines.

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“…Shin et al introduced a new membrane de-wetting technique by using high temperature air and determined the optimum conditions by using the RSM (Y. Shin, J. Choi, T. Lee, J. Sohn, 2016). Cao et al (Cao et al, 2016) developed an artificial neural network (ANN) model to study the performance of VMD desalination process under different operating parameters such as the feed inlet temperature, the vacuum pressure, the feed flow rate, and the feed salt concentration. However, as 1 far as authors' knowledge, the RSM method has not been used in modeling and optimization of 2 VMD process using both operating conditions and module parameters as variables.…”

Section: Introductionmentioning

confidence: 99%

Modeling and multi-objective optimization of vacuum membrane distillation for enhancement of water productivity and thermal efficiency in desalination

Cheng

Zhang

2018

Chemical Engineering Research and Design

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Water productivity and thermal efficiency in membrane distillation (MD) have been the main research targets, for the aims of commercial application in desalination. The comprehensive understanding of the influence of module configuration parameters, operating conditions and their interaction on MD performance is the key for MD commercialization. In this paper, the multi-objective modeling and optimization in the vacuum membrane distillation were performed by response surface methodology and desirability function approach. A series of PVDF hollow fiber modules of different scale were used to provide the essential data and to verify the modeling program. The multi-objectives including water permeate flux (J), water productivity per unit volume of module (Pv), gained output ratio (GOR), and a comprehensive index (Dm) assessing the 10 desired MD performances were predicted and experimentally verified. The influence of operating 11 parameters (temperature, velocity, and concentration of feed) and membrane module parameters 12 (membrane packing density and length-diameter ratio of module) and their binary interactions on the multi-objectives was investigated. It is found that among the investigated factors, feed inlet temperature and its interaction effect with module parameters play dominant roles on MD performance. Under the multi-objective optimum conditions, 4.85×10 3 kg/(m 3 •h) of Pv and 0.91 16 of GOR were achieved within the investigated range. Water productivity and thermal efficiency 17 can be simultaneously enhanced by optimizing operating and module conditions with the approach 18 developed in this study. 19

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Modeling and simulation of VMD desalination process by ANN

Cited by 66 publications

References 18 publications

Modeling of cutting performances in turning process using artificial neural networks

Modeling of cutting performances in turning process using artificial neural networks

Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants

Modeling and multi-objective optimization of vacuum membrane distillation for enhancement of water productivity and thermal efficiency in desalination

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