Novel adsorption model of filtration process in polycarbonate track-etched membrane: Comparative study

Abstract

Current assumptions are used in the formulation of pseudo-first (PFO) and second-order (PSO) models to describe the kinetic data of filtration based on ideal operating conditions. This paper presents a new model developed with pseudo <i>n<sup>th</sup></i> order and based on real assumption. A comparison was performed between PFO, PSO and the new model to highlight their performance and the optimisation of the pseudo-order equation, using MATLAB software. Adsorption characteristic of bovine serum albumin adsorption on the track-etched membrane are used as a medium based on protein filtration data were extracted from the literature for different concentrations to demonstrate the comparison between PFO/PSO and the new model. The pseudo first and second-order kinetic models were applied to test the experimental data and they did not provide reasonable values. The results show that the predicted values are consistent with experimental values giving a good correlation coefficient <i>R</i><sup>2</sup> = 0.997 and a minimum root mean squared error RMSE = 0.0171. Indeed, the experimental results follow the new model and the optimal pseudo equation order <i>n</i> = 1.115, the most suitable curves for the new model. As a result, we used different experimental adsorption data from the literature to examine and check the applicability and validity of the model.

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

“…The experimental data used to construct the ANN model for NF/RO seawater desalination plant are summarized in Table 1 However, the function, produces output in the range of -1 to +1 and the linear transfer function produce outputs in the range of -∞ to +∞. [34,35].…”

“…In order to evaluate the quality of the prediction of ANN and MLR models, there are set of evaluation criteria such as the coefficient of correlation R, mean absolute error (MAE) and root mean square error (RMSE) [38] for the prediction of permeate conductivity, recovery and permeate flow rate, also the average absolute relative deviation [35,39]. The equations are expressed in below:…”

Investigation of the Efficiency of Small-Scale NF/RO Seawater Desalination by Using Artificial Neural Network Modeling

“…The correlation coefficient R2, the root means square error RMSE (square root of the average sum of squares), and mean absolute error (MAE) are the statistical parameters commonly used in the literature [26][27][28][29] for error calculation. They are calculated according to the following equations:…”

“…According to each input that exceeds 6% [28,34], all the information has a considerable impact on the output (permeate flow rate and permeate water recovery). In the present study, the time has an effect of about 19% on both outcomes.…”

Managing and Control of Nanofiltration / Reverse Osmosis Desalination System: Application of Artificial Neural Network

“…(6)). However, the (logsig) function, produces output in the range of -1 to +1 and the tansig transfer function produces outputs in the range of -∞ to +∞ [26].…”