Application of Response Surface Methodology and Experimental Design in Direct Contact Membrane Distillation

2007 ◽  
Vol 46 (17) ◽  
pp. 5673-5685 ◽  
Author(s):  
Mohamed Khayet ◽  
Cornel Cojocaru ◽  
Carmen García-Payo
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Direct Contact ◽  
Membrane Distillation ◽  
Direct Contact Membrane Distillation

scholarly journals Employing full factorial design and response surface methodology for optimizing direct contact membrane distillation operational conditions in desalinating the rejected stream of a reverse osmosis unit at Esfahan refinery–Iran

2018 ◽  
Vol 19 (2) ◽  
pp. 492-501 ◽  
Author(s):  
M. Ebadi ◽  
M. R. Mozdianfard ◽  
M. Aliabadi
Keyword(s):  
Response Surface Methodology ◽  
Reverse Osmosis ◽  
Flow Rate ◽  
Direct Contact ◽  
Membrane Distillation ◽  
Permeate Flux ◽  
Operational Conditions ◽  
Direct Contact Membrane Distillation ◽  
Full Factorial ◽  
Feed Temperature

Abstract Optimized condition for desalination of the reverse osmosis (RO) rejected stream from Esfahan Oil Refining Company (EORC) using direct contact membrane distillation (DCMD) with polytetrafluoroethylene (PTFE) membrane was investigated here, having designed a set of 34 experiments using response surface methodology (RSM) and full factorial design (FFD) modelling, carried out in a laboratory scale set-up built for this purpose. Statistical criteria for validation, significance, accuracy and adequacy confirmed the suitability of the quadratic polynomial model employed. Response plots and regression equations suggested that the permeate flux response improved with increased feed temperature, reduced permeate temperature and enhanced feed flow rate. Optimizing DCMD process showed that maximum permeate flux of 60.76 L/m2·h could be achieved at the following optimum operational conditions: feed temperature and flow rate of 70 °C and 2 L/min, respectively, as well as the permeate temperature of 15 °C. At this point, the mean annual energy required for 90% water recovery (36 m3/h off the RO brackish rejected stream) at EORC refinery was found to be 96 GJ, which could be supplied using solar or conventional energy systems at Isfahan, facing a very critical water shortage at present.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

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