Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

Abstract

The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb 2+ , Hg 2+ , Cd 2+ , Cu 2+ , Zn 2+ , Ni 2+ , Cr 4+ } on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflower stem). Results show that support vector regression (SVR) performed slightly better, more accurate, stable, and more rapid than least-square support vector regression (LS-SVR) and artificial neural networks (ANN). The SVR model is highly recommended for estimating the ternary adsorption kinetics of a multicomponent system.

Virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms as influenced by particle size and properties

“…SVM has several advantages compared to ANN, including its ability to avoid being trapped in local minima by mapping the nonlinear relationship between inputs and output(s), solving problems using only support vectors, and handling small data sets [37]. According to Yettou [39], the performance of an SVM model depends on the choice of kernel function and its parameters. The predicted output can be obtained using the SVM model as Equation 3.…”

Multicomponent Adsorption Capacity Forecasting Based on Support Vector Machine with Dragonfly Algorithm

“…10 Due to this complexity, machine learning algorithms have emerged as a powerful tool, compared to other classical methods, to tackle the nonlinear relationships directly from samples with no previous knowledge of the chemical or physical nature that affects the system. [10][11][12][13] Different machine learning algorithms were used in the literature as an advanced mathematical tool to model the adsorption capacity of single and multicomponent adsorption systems, such as: artificial neural network (ANN), 3,7,[11][12][13][14][15] support vector machine (SVM). 12,[15][16][17] The SVM method can overcome some disadvantages of the ANN model, such as robustness, and avoid the result of falling into local optimum.…”

“…[10][11][12][13] Different machine learning algorithms were used in the literature as an advanced mathematical tool to model the adsorption capacity of single and multicomponent adsorption systems, such as: artificial neural network (ANN), 3,7,[11][12][13][14][15] support vector machine (SVM). 12,[15][16][17] The SVM method can overcome some disadvantages of the ANN model, such as robustness, and avoid the result of falling into local optimum. 18 However, the SVM's parameters are tuned using a built-in optimisers, which are generally selected by trial and error method.…”