Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

Abstract

Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta-naphthol using the titanium dioxide (TiO 2 ) nanoparticles were conducted in this study. Biphasic TiO 2 nanoparticles were synthesized using the controlled hydrolysis of TiCl 4 , and their properties were studied using the Xray diffraction and transmission electron microscopy methods. Therefore, factors affecting photocatalytic degradation of beta-naphthol including impurity concentration, catalyst content, acidity, and aeration rate were monitored and controlled. The laboratory data showed that degradation rate of beta-naphthol is a complicated nonlinear function of monitored variables. Two models including artificial network trained with particle swarm optimization (ANN-PSO) and adaptive neuro-fuzzy interference system trained with particle swarm optimization (ANFIS-PSO) were used for prediction of this system. The results showed presence of a significant relation between the real and predicted data of these 2 models. However, ANFIS-PSO can be more efficiently applied for prediction and optimization of photocatalytic behavior of TiO 2 nanoparticles as for degradation of beta-naphthol as compared to ANN-PSO. As an advantage, ANFIS eliminates the problems of fuzzy logic, such as creation of membership functions, and local minima, which should be located in design of ANN, and through PSO algorithm, it could be a very powerful tool for simulating kinds of processes.

Modelling and optimization of nanovector synthesis for applications in drug delivery systems

“…A comparison between ANFIS and MLP-ANN was made. ANFIS was chosen because it showed better performance than MLP-ANN [22] and MLP-ANN was chosen because it is the most common machine learning algorithm used as shown in tables ? ?, 1 and 2.…”

“…AI algorithms used for modelling are increasing in popularity because due to the lack of variable assumptions, these methods are less restrictive and are capable of recognizing and modelling non-polynomial relations between the variables [18]. Examples of AI used for NV synthesis include multilayer perceptron artificial neural networks (MLP-ANN) [19][20][21], adaptive neurofuzzy inference system (ANFIS) [22], support vector machines (SVMs) [23,24], genetic proggraming (GP) [25,26], decision trees [27], and random forests (RF) [28,29].…”

“…Metaheuristic algorithms that have been used in processes involving nanomaterials are: genetic algorithms (GA) [31][32][33], particle swarm optimization (PSO) [22,24,34], simulated annealing (SA) [35], differential evolution (DE) [36,37], ant colony optimization (ACO) [38], bees algorithm (BA) [39], firefly algorithm (FA) [40], cuckoo search (CS) [41], bat algorithm [42], gravitational search algorithm (GSA) [43] , symbiotic organism search (SOS) [44], whale optimization algorithm (WOA) [45] and grasshopper optimization algorithm (GOA) [45]. Deciding which algorithm to use is no easy task because the no free lunch theorem for optimization states that algorithms which perform better for certain optimization problems will have a lower performance for other class of problems, and therefore there is no overall best algorithm [46].…”

Studies on the titanium dioxide nanoparticles: biosynthesis, applications and remediation

“…Similarly, there is a report where TiO 2 -activated carbon composites have application in photo catalytic degradation of β-naphthol from the waste water [11]. The photo catalytic behavior of TiO 2 has been studied for the degradation of beta-naphthol using x-ray diffraction and TEM [12].…”