Application of Artificial Neural Network, Fuzzy Inference System and Adaptive Neuro-Fuzzy Inference System to Predict the Removal of Pb(II) Ions from the Aqueous Solution by Using Magnetic Graphene/Nylon 6 Mohammad Afroozeh 1 , Mahmoud Reza Sohrabi 2* , Mehran Davallo 3 , Seyed Yadollah Mirnezami 3 , Fereshteh Motiee 3 and Morteza Khosravi 3 1 Analytical Chemistry, Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran 2 Department of Chemistry, Islamic Azad University, Tehran, Iran 3 Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran *Corresponding author: Sohrabi MR, Department of Chemistry, Islamic Azad University, Tehran, Iran, Tel: 0098-21-7700 98 36-42; E-mail: Sohrabi.m46@yahoo.com Received date: March 28, 2018; Accepted date: April 02, 2018; Published date: April 26, 2018 Copyright: © 2018 Afroozeh M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract In this study, three modeling techniques based on artificial intelligence were used to predict the removal percent of lead(II) ions from the aqueous solution. These models include Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Magnetic graphene adsorbent supported on nylon 6 was used for removing lead(II) ions. Optimal conditions for the experimental parameters were performed using the Taguchi methodology. The analysis of variance (ANOVA) test at the 95% confidence level was applied to the results of these models which suggested there were no significant differences among these models. Keywords: Artifcial intelligence models; Magnetic graphene; Nylon6; Lead(II) ions Introduction Modeling of chemical processes can lead to lower costs during testing. By using the ability of these models, we can predict the optimal conditions for a process. Intelligent computer systems are the suitable tool that can be employed for this purpose. Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are part of the intelligent models. ANN is a non-linear statistical data modeling that is inspired by biological neurons. Each neuron is related to a mathematical function with determined inputs, a scientific computation method, and outputs [1,2]. Fuzzy set theory was introduced by Lotfi A. Zadeh in 1965. Fuzzy inference is a process of mapping from a given input to an output data set using the theory of fuzzy sets [3]. On the other hand, ANFIS is a fuzzy inference system combined with the computational power of ANN and acts as an adaptive multilayer feed-forward network. ANFIS is a potent approach to modeling the input and output relationship in non-linear and complex systems [4]. Heavy metals can enter a water supply by industrial and consumer waste by means of various human activity such as mining, fertilizer industries, refning ores, tanneries, battery manufacturing, paper industries, metal-based pesticides and fuels, or even from acidic rain breaking down soils and releasing heavy metals into streams, lakes, rivers, and groundwater [5,6]. Among heavy metals, lead is considered as longstanding environmental contaminant. Lead in large dosage can seriously harm human life and aquatic ecosystems. Where exceeding the permissible concentration limit of lead in the human body can end up with acute or chronic problems such as mental retardation, seizures, anemia, kidney and liver disorder, cancer and hepatitis [7-9]. Terefore, removal of Pb(II) ions from the aqueous system is very important. Tere are various methods that have been explored to decrease lead(II) ions, namely membrane fltration, chemical precipitation, solvent extraction, ion-exchange, oxidation/reduction, electrode deposition, and bio-adsorption [10-12]. However, these methods have some limitations. Among these methods, adsorption process has been widely used because it is a simple and relatively economical process [13,14]. Graphene (G) as an adsorbent is a two- dimensional form and honeycomb with sp2-bound carbon atoms [15]. It has many unique properties such as singular high room temperature, very high thermal conductivity, fexibility and tensile resistance. Tis feature has made it a potential adsorbent for the removal of heavy metal ions like lead from the aqueous solution [16-18]. Graphene is produced from graphite based on Hummers and ofeman’s procedure [19]. In this study, Artifcial Neural Network with three algorithms, Fuzzy Inference System and Adaptive Neuro-Fuzzy Inference System have been used for predicting the removal percent of lead ions from the aqueous solution using magnetic graphene oxide supported on nylon 6. Te efects of various experimental parameters such as pH of the solution, initial Pb(II) ions concentration, and adsorbent dosage were investigated based on Taguchi experimental design to optimize the absorption performance. To evaluate the signifcant diferences between the reported variances of recovery for removing lead(II) ions the results of ANN, FIS and ANFIS models were compared using ANOVA analysis. Teory Artifcial neural networks model Artifcial Neural Networks include a contiguous network of nodes, which are separated into many layers. Te input and output layers in the neural networks make the main structure of artifcial neural networks. In addition, there are a series of hidden layers between input and output layers. Te main duty of hidden layers is to evaluate the relationship between unknown and complex by iterative training from many input-output pairs. Te hidden layer has some nodes that they have activation functions and numeric weights that are controlled by C h e m i c a l S c i e n c e s J o u r n a l ISSN: 2150-3494 Chemical Sciences Journal Afroozeh et al., Chem Sci J 2018, 9:2 DOI: 10.4172/2150-3494.1000185 Research Article Open Access Chem Sci J, an open access journal ISSN: 2150-3494 Volume 9 • Issue 2 • 1000185