Optimization and Prediction of Stability of Emulsified Liquid Membrane (ELM): Artificial Neural Network

Abstract

In this work, the emulsified liquid membrane (ELM) extraction process was studied as a technique for separating different pollutants from an aqueous solution. The emulsified liquid membrane used consisted of Sorbitan mono-oleate (Span 80) as a surfactant with n-hexane (C6H14) as a diluent; the internal phase used was nitric acid (HNO3). The major constraint in the implementation of the extraction process by an emulsified liquid membrane (ELM) is the stability of the emulsion. However, this study focused first on controlling the stability of the emulsion by optimizing many operational factors, which have a direct impact on the stability of the membrane. Among the important parameters that cause membrane breakage, the surfactant concentration, the emulsification time, and the stirring speed were demonstrated. The optimization results obtained showed that the rupture rate (Tr) decreased until reaching a minimum value of 0.07% at 2% of weight/weight of Span 80 concentration with an emulsification time of 3 min and a stirring speed of 250 rpm. On the other hand, the volume of the inner phase leaking into the outer phase was predicted using an artificial neural network (ANN). The evaluation criteria of the ANN model in terms of statistical coefficient and RMSE error revealed very interesting results and the performance of the model since the statistical coefficients were very high and close to 1 in the four phases (R_training = 0.99724; R_validation = 0.99802; R_test = 0.99852; R_all data = 0.99772), and also, statistical errors of RMSE were minimal (RMSE_training= 0.0378; RMSE_validation = 0.0420; RMSE_test = 0.0509; RMSE_all data = 0.0406).

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

“…These neurons work together to form the network, and the functionality of the network is primarily determined by the connections between the neurons [ 16 , 17 ]. The neurons in an ANN are divided into three separate layers: the input layer, the output layer, and the hidden layer [ 18 , 19 ]. The input layer contains the same number of neurons as there are input variables, while the output layer has a corresponding number of neurons to the output variables [ 18 ].…”

“…The neurons in an ANN are divided into three separate layers: the input layer, the output layer, and the hidden layer [ 18 , 19 ]. The input layer contains the same number of neurons as there are input variables, while the output layer has a corresponding number of neurons to the output variables [ 18 ]. In between the input and output layers is at least one hidden layer, the number of neurons, which is dependent on the specific algorithm used in the ANN [ 18 ].…”

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

“…The quality of the developed models was examined using statistical analysis and ANOVA at a 95% confidence level. Various model quality measures, such as the p-value, F-value, degree of freedom (DF), coefficient of determination (R 2 ), adjusted determination of coefficient (R adj 2 ), and Root Mean Square Error (RMSE), were used to evaluate the statistical adequacy of the models [15,25,[27][28][29][30][31][32][33][34][35]. The F-value describes the variation in the responses, which can be evaluated using a regression equation, whereas the p-value indicates the statistical adequacy of the developed model.…”

Modelling and Comparative Analysis of Different Methods of Liquid Membrane Separations

“…The conventional extraction-stripping process includes an extraction column and a stripping column; in the film pertraction, three phases flow as parallel films [ 1 , 2 , 3 ]. In the emulsion membrane extraction, two options are possible: (1) the aqueous stripping phase is encapsulated as microdroplets in large droplets of a liquid membrane moving in a continuous aqueous feed solution [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]; and (2) the aqueous feed phase is encapsulated as microdroplets in large droplets of the liquid membrane phase rising or falling in the continuous stripping phase. Emulsion liquid membranes are double emulsions because they consist of water–oil–water or oil–water–oil systems.…”

“…Surfactants are often used to stabilize emulsions by micelle formation. Emulsion membranes have some advantages over the extraction method, such as easy operation, suitability for low and high concentrations of metal ions, simultaneous extraction and distillation, low power consumption, and low cost: the liquid membrane, which includes both the carrier and the surfactant, can be reused at the end of the process [ 5 ]. However, due to low productivity, this method also cannot be used in large-scale separation processes.…”