Experimental Study and Machine Learning Simulation of Pb (Ii) Separation from Aqueous Solutions Via a Nanostructure Adsorbent

Abstract

In this study, a zeolite ZSM-5/silica aerogel (ZSM5/SA) composite was synthesized as a mesoporous adsorbent in three different ratios (including 25, 50, and 75% silica aerogel). Then the characterization of adsorbents was performed by Field-Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-Ray (EDX), and Surface area and pore size (BET-BJH) analyses. Adsorbents were tested under the same conditions to compare the adsorption capacities. After data collection, multilayer linear regression (MLR) was used to predict the removal of lead ions from an aqueous solution. For this purpose, multilayer artificial neural networks (MLP-ANN) with error backpropagation functions and support vector regression (SVR) were coded with MATLAB software. The effect of effective parameters on the adsorption process was investigated using experimental design and the general 2K full factorial method. By ANOVA analysis of variance, the linear regression equation was obtained with a prediction accuracy of 87.3%. The best-predicted percentages (with 70% data) for ANN and SVR were 99.52% and 98.43%, respectively. In addition, the mean square error (MSE) for ANN and SVR was 0.00037 and 0.0083, respectively. Neural network training functions were compared using ANN model performance (MSE, SSE, SAE, MAE) and R2. Examination of neural network and SVR predictive behavior showed that SVR performs data training better, but neural network data testing performs 1.16% better than SVR. Data prediction performance indicated that neural networks and SVR were successful in data prediction.

Keywords: Zeolite ZSM-5, Silica aerogel, machine learning, Separation, Adsorption, Lead