# ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

^{*}

## Abstract

**:**

_{2}process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO

_{2}nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO

_{2}, and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme.

## 1. Introduction

_{2}process. Here we investigate the photocatalytic proficiency of the Ag-TiO

_{2}particles for elimination of AY23 as a refractory pollutant. The outcome of usable prime factors to be mentioned as the initial dye concentration, ultraviolet (UV) light intensity, irradiation time, as well as the dosage of Ag-TiO

_{2}nanoparticles have been discussed. A few sets of data extracted from the literature are implemented, and the ANN and ICA methodologies are generalized on the basis of predictive models in order to eliminate AY23 in water by utilizing a set of chosen water quality criteria. Predictive potencies of the generalized models are verified by utilizing multiple statistical performance criteria parameters. Both ANN and ICA modeling methods display good predictions in this study. The ANN model is more precise when compared with ICA model. This paper is one of the first attempts in the developing of ANN and ICA modeling methods for the removal of AY23 in water by the UV/Ag-TiO

_{2}process.

## 2. Literature Survey

_{2}as a photocatalyst has been conducted for the reason of its immense chemical stability, without any toxicity, low cost, and exquisite deterioration for organic pollutants [18]. The growth of UV/TiO

_{2}processes to attain entire mineralization associated with organic pollutants were investigated thoroughly for a broad variety of industrial dyes [19]. Heterogeneous photocatalysis through amalgamation of TiO

_{2}and UV light is considered as one of the promising AOPs for the devastation of water-soluble organic pollutants observed in water, as well as wastewater.

_{2}-nanoparticles, due to their high specific surface area, are suitable for pollutant removal. An ANN has been utilized for the precise prediction of percentages of brilliant green, crystal violet, and methylene blue dye removal from aqueous solutions using MnO

_{2}-NP-AC adsorbent [22]. The nano-adsorbents, by having high specific surface area and accessible surface adsorption sites, are more capable for the adsorption of pollutants. The γ-Fe

_{2}O

_{3}nanoparticle-loaded activated carbon is applied for the ultrasound-assisted simultaneous removal of dyes from aqueous solution, and the relative variable importance on adsorption in the batch system is studied using RSM and ANN [23]. A RSM and ANN have been utilized for removal of ternary toxic dyes onto copper sulfide nanoparticles loaded on activated carbon [24]. ANN and RSM have been proposed for modeling and interaction of the variables for the maximum removal percentage of ternary dyes utilizing the experimental data on the basis of the central composite design [25].

## 3. Materials and Methods

#### 3.1. Materials

_{3}H

_{7})

_{4}, methanol (MeOH), as well as silver nitrate (AgNO

_{3)}, were acquired from Merck (Darmstadt, Germany) and utilized without any additional purifications. AY23 was purchased from Acros (New York, USA) and utilized without additional purification. Figure 1 displays the chemical structure of this dye. Deionized water is employed throughout the work.

#### 3.2. Analytical Technique

_{2}, AY23 is utilized as the pollutant. Sample solutions are sonicated before irradiation for 4 min. At known irradiation time intervals, the samples (4 mL) are removed and, afterward, analysis is carried out by UV-visible (V) spectrophotometry (Ultrospec 2000, Biotech Pharmacia, Little Chalfont, UK) at 427 nm. A correlation based on linearity is laid down in the midst of the AY23 concentration, as well as the absorbance, which are in the range 0–50 mg/L holding a correlation coefficient, ${R}^{2}\text{}=\text{}0.9981$. Equation (1) is utilized in order to compute the photocatalytic eradication effectiveness (R, %) in the experiments:

_{0}(mg/mL) as well as C

_{t}(mg/mL), are taken to be the primary concentration of AY23 and the concentration associated with AY23 at the duration $t$, respectively.

#### 3.3. Artificial Neural Network Method

_{2}process (Figure 2). The input variables of the neural network are stated as initial concentration of dye (mg/L), UV light intensity $\left(\mathrm{W}/{\mathrm{m}}^{2}\right),$ initial dosage of nano Ag-TiO

_{2}(mg/L), irradiation time (min). AY23 eradication percentage (R, %) is chosen as the experimental response or output variable.

#### 3.4. Imperialist Competitive Algorithm Method

_{var}-dimensional optimization problem, a country is demonstrated by 1 × N

_{var}array. This array is stated as below:

#### 3.5. The Dataset

_{2}, as well as irradiation time are selected as the input variables and the eradication of AY23 as the output variable. The range of variables is summarized in Table 1.

## 4. Models, Results, and Discussion

#### 4.1. Results and Discussion

^{2}, the root mean squared error (RMSE), the factor of accuracy A

_{f}, as well as the Nash–Sutcliffe coefficient associated with the efficiency E

_{f}. The chosen validated specifications are stated in the following form [31]:

^{2}exhibits the level of variability which is possible to be stated using the models, along with RMSE, which depicts the evaluation of the average error in forecasting related to the dependent variable. The preciseness factor A

_{f}, a straightforward multiplicative factor exhibits the diffusion of outcomes around the forecast. A higher value of A

_{f}will result in the minimal preciseness of the average estimation. The numerical value shows that there exists a flawless consent among all the forecasted and evaluated values [32]. The Nash-Sutcliffe coefficient of efficiency E

_{f}, shows that the model fit is a generalized evaluation [$-\infty $; 1], which compares the MSE produced with the help of a distinct simulated model to the variance of the target output sequence [33].

^{2}value above 0.8 states that both the training and validation set are notably correlated. The A

_{f}and E

_{f}values are close to unity and the low RMSE affirms the superior extension and predictive capabilities of the two modeling techniques for the given dataset.

_{j}is termed as the relative significance of the jth input variable on the output variable. W

_{S}are termed as the connection weights. N

_{i}and N

_{h}are the numbers of inputs, and hidden neurons, respectively. The superscripts i, h, and o signify the input, hidden, and output layers, respectively. Subscripts k, m and n signify the input, hidden, and as output neurons, respectively.

## 5. Concluding Remarks

_{2}operation is researched. Predictive and universalization abilities of the ANN and ICA models in order to eliminate the AY23 in water are investigated by the implementation of a statistically-designed dataset gathered from the literature. The initial concentration of dye, UV light intensity, initial dosage, of nano Ag-TiO

_{2}, as well as irradiation time, are utilized as predictor variables.

^{2}, A

_{f}, and E

_{f}. Both ANN and ICA modeling methods display good prediction in this work. The ANN model is more precise when compared with ICA model. The optimum conditions, as well as the relative variable importance for each variable on the AY23 removal efficiency are represented. This paper has a significant contribution in initializing a superior starting point for the removal of AY23 in water by the UV/Ag-TiO

_{2}process. As the progress of artificial intelligence methodologies have been affected significantly by a deficiency of training techniques, it is taken into consideration that our schemes cover up this emptiness, and it is our hope that they will result in several new applications.

## Author Contributions

## Conflicts of Interest

## References

- Behnajady, M.A.; Modirshahla, N.; Ghanbary, F. A kinetic model for the decolorization of C.I. Acid Yellow 23 by Fenton process. J. Hazard. Mater.
**2007**, 148, 98–102. [Google Scholar] [CrossRef] [PubMed] - Castro, A.L.; Nunes, M.R.; Carvalho, A.P.; Costa, F.M.; Floreencio, M.H. Synthesis of anatase TiO
_{2}nanoparticles with high temperature, stability and photocatalytic activity. Solid State Sci.**2008**, 10, 602–606. [Google Scholar] [CrossRef] - Bagheri, A.R.; Ghaedi, M.; Hajati, S.; Ghaedi, A.M.; Goudarzi, A.; Asfaram, A. Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach. RSC Adv.
**2015**, 5, 59335–59343. [Google Scholar] [CrossRef] - Chang, Y.; Erera, A.L.; White, C.C. Risk assessment of deliberate contamination of food production facilities. IEEE Trans. Syst. Man Cybern. Syst.
**2015**, 47, 381–393. [Google Scholar] [CrossRef] - Dil, E.A.; Ghaedi, M.; Asfaram, A.; Hajati, S.; Mehrabi, F.; Goudarzi, A. Preparation of nanomaterials for the ultrasound-enhanced removal of Pb
^{2+}ions and malachite green dye: Chemometric optimization and modeling. Ultrason. Sonochem.**2017**, 34, 677–691. [Google Scholar] [CrossRef] [PubMed] - Fan, C.T.; Wang, Y.K.; Huang, C.R. Heterogeneous information fusion and visualization for a large-scale intelligent video surveillance system. IEEE Trans. Syst. Man Cybern. Syst.
**2016**, 47, 593–604. [Google Scholar] [CrossRef] - Jafari, R.; Yu, W. Fuzzy Control for Uncertainty Nonlinear Systems with Dual Fuzzy Equations. J. Intell. Fuzzy. Syst.
**2015**, 29, 1229–1240. [Google Scholar] [CrossRef] - Jafari, R.; Yu, W. Fuzzy Differential Equation for Nonlinear System Modeling with Bernstein Neural Networks. IEEE Access
**2017**, 4, 9428–9436. [Google Scholar] [CrossRef] - Jafari, R.; Yu, W. Uncertainty Nonlinear Systems Modeling with Fuzzy Equations. Math. Probl. Eng.
**2017**, 2017. [Google Scholar] [CrossRef] - Jafarian, A.; Jafari, R.; Khalili, A.; Baleanud, D. Solving fully fuzzy polynomials using feed-back neural networks. Int. J. Comp. Math.
**2015**, 92, 742–755. [Google Scholar] [CrossRef] - Khataee, A.R. Photocatalytic removal of C.I. Basic Red 46 on immobilized TiO
_{2}nanoparticles: Artificial neural network modeling. Environ. Technol.**2009**, 30, 1155–1168. [Google Scholar] [CrossRef] [PubMed] - Shirvani Ardekani, P.; Karimi, H.; Ghaedi, M.; Asfaram, A.; Kumar Purkait, M. Ultrasonic assisted removal of methylene blue on ultrasonically synthesized zinc hydroxide nanoparticles on activated carbon prepared from wood of cherry tree: Experimental design methodology and artificial neural network. J. Mol. Liq.
**2017**, 229, 114–124. [Google Scholar] [CrossRef] - Kunwar, P.; Shikha, G. Artificial intelligence based modeling for predicting the disinfection by-products in water. Chemom. Intell. Lab. Syst.
**2012**, 114, 122–131. [Google Scholar] - Niknam, T.; Taherian Fard, E.; Pourjafarian, N.; Rousta, A. An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng. Appl. Artif. Intell.
**2011**, 24, 306–317. [Google Scholar] [CrossRef] - Yousefi, M.; Darus, A.N.; Mohammadi, H. An imperialist competitive algorithm for optimal design of plate-fin heat exchangers. Int. J. Heat Mass Transf.
**2012**, 55, 3178–3185. [Google Scholar] [CrossRef] - Atashpaz-Gargari, E.; Lucas, C. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007; pp. 4661–4667. [Google Scholar]
- Khajeh, M.; Kaykhaii, M.; Sharafi, A. Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water sample. J. Ind. Eng. Chem.
**2013**, 19, 1624–1630. [Google Scholar] [CrossRef] - Wang, H.W.; Lin, H.C.; Kuo, C.H.; Cheng, Y.L.; Yeh, Y.C. Synthesis and photocatalysis of mesoporous anatase TiO
_{2}powders incorporated Ag nanoparticles. J. Phys. Chem. Solids**2008**, 69, 633–635. [Google Scholar] [CrossRef] - Behnajady, M.A.; Modirshahla, N.; Daneshvar, N.; Rabbani, M. Photocatalytic degradation of an azo dye in a tubular continuous-flow photoreactor with immobilized TiO
_{2}on glass plates. J. Chem. Eng.**2007**, 127, 167–176. [Google Scholar] [CrossRef] - Mazaheri, H.; Ghaedi, M.; Ahmadi Azqhandi, M.H.; Asfaram, A. Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(II) removal from a binary aqueous solution by natural walnut carbon. Phys. Chem. Chem. Phys.
**2017**, 19, 11299–11317. [Google Scholar] [CrossRef] [PubMed] - Asfaram, A.; Ghaedi, M.; Ahmadi Azqhandi, M.H.; Goudarzi, A.; Dastkhoon, M. Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye. RSC Adv.
**2016**, 6, 40502–40516. [Google Scholar] [CrossRef] - Asfaram, A.; Ghaedi, M.; Hajati, S.; Goudarzi, A. Ternary dye adsorption onto MnO2 nanoparticle-loaded activated carbon: derivative spectrophotometry and modeling. RSC Adv.
**2015**, 5, 72300–72320. [Google Scholar] [CrossRef] - Asfaram, A.; Ghaedi, M.; Hajati, S.; Goudarzi, A. Synthesis of magnetic γ-Fe
_{2}O_{3}-based nanomaterial for ultrasonic assisted dyes adsorption: Modeling and optimization. Ultrason. Sonochem.**2016**, 32, 418–431. [Google Scholar] [CrossRef] [PubMed] - Bagheri, A.R.; Ghaedi, M.; Asfaram, A.; Hajati, S.; Ghaedi, A.M.; Bazrafshan, A. Modeling and optimization of simultaneous removal of ternary dyes onto copper sulfide nanoparticles loaded on activated carbon using second-derivative spectrophotometry. J. Taiwan Inst. Chem. Eng.
**2016**, 65, 212–224. [Google Scholar] [CrossRef] - Azad, F.N.; Ghaedi, M.; Asfaram, A.; Jamshidi, A.; Hassani, G.; Goudarzi, A. Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO (OH)-NWsAC using response surface methodology and an artificial neural network. RSC Adv.
**2016**, 6, 19768–19779. [Google Scholar] [CrossRef] - Chen, C.L.P.; Zhang, T.; Tam, S.C. A novel evolutionary algorithm solving optimization problems. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA, 5–8 October 2014. [Google Scholar]
- Puntonet, C.G.; Grriz, J.M.; Salmern, M.; Hornillo-Mellado, S. Theoretical method for solving BSS-ICA using SVM. In Proceeding of the International Conference on Independent Component Analysis and Signal Separation, Granada, Spain, 22–24 September 2004; pp. 256–262. [Google Scholar]
- Gorriz, J.M.; Puntonet, C.G.; Salmeron, M.; Ortega, J. New method for filtered ICA signals applied to volatile time series. In Proceedings of the 7th International Work Conference on Artificial and Natural Neural Networks IWANN 2003 Lecture Notes in Computer Science, Menorca, Spain, 3–6 June 2003; Volume 2687, pp. 433–440. [Google Scholar]
- Pothiya, S.; Ngamroo, I.; Kongprawechnon, W. Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers. Manag.
**2008**, 49, 506–516. [Google Scholar] [CrossRef] - Ghanbary, F.; Jafarian, A. Preparation and photocatalytic properties of silver doped titanium dioxide nanoparticles and using artificial neural network for modeling of photocatalytic activity. J. Basic Appl. Sci.
**2012**, 12, 2889–2898. [Google Scholar] - Karul, C.; Soyupak, S.; Clesiz, A.F.; Akbay, N.; German, E. Case studies on the use of neural networks in eutrophication modeling. Ecol. Model.
**2000**, 134, 145–152. [Google Scholar] [CrossRef] - Smith, G.N. Probability and Statistics in Civil Engineering; Collins: London, UK, 1986. [Google Scholar]
- Nash, J.E.; Sutcliffe, I.V. River flow forecasting through conceptual models Part I—A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Slokar, Y.M.; Zupan, J.; Marechal, A.M.L. The use of artificial neural network (ANN) for modeling of the H2O2/UV decoloration process. Dyes Pigment
**1999**, 42, 123–135. [Google Scholar] [CrossRef] - Aleboyeh, A.; Kasiri, M.B.; Olya, M.E.; Aleboyeh, H. Prediction of azo dye decolorization by UV/H
_{2}O_{2}using artificial neural networks. Dyes Pigment**2008**, 77, 288–294. [Google Scholar] [CrossRef]

**Figure 2.**Schematic diagram of the artificial neural network (ANN) modeling approaches. CR: eradication percentage of AY23; UV light: ultraviolet light.

**Figure 3.**Implication of the quantity of neurons embedded in the hidden layer on the efficiency of the (

**A**) ANN and (

**B**) imperialist competitive algorithm (ICA) modeling schemes.

**Figure 4.**The measured and predicted eradication of AY23 in water using (

**A**) ANN and (

**B**) ICA schemes in training of the set based on the best optimum number of hidden layer neurons for the selected algorithms.

**Figure 5.**The measured and predicted eradication of AY23 in water using (

**A**) ANN and (

**B**) ICA schemes in validation of the set based on the best optimum number of hidden layer neurons for the selected algorithm.

Variable | Range |
---|---|

Input layer | |

Ag-TiO_{2} initial dosage (g/L) | 0.01–0.05 |

AY23 initial concentration (mg/L) | 5–60 |

UV light intensity (W/m^{2}) | 0–60 |

Irradiation time (min) | 0–60 |

Output layer | |

Removal of AY23 (%) | 0–100 |

Model | Sub-set | RMSE | E_{f} | A_{f} | R^{2} |
---|---|---|---|---|---|

ANN | Training | 0.04039 | 1.01256 | 1.00103 | 1.00685 |

Validation | 0.08076 | 1.04562 | 0.99001 | 1.02212 | |

ICA | Training | 0.18345 | 0.95236 | 0.97852 | 0.94670 |

Validation | 0.19884 | 0.93545 | 0.94256 | 0.92575 |

^{2}: determination coefficient.

W1 Neuron | [Ag-TiO_{2}]_{0} | [AY23]_{0} | UV light | Time | Bias | W2 Neuron | Weight |
---|---|---|---|---|---|---|---|

2 | −0.082 | 3.940 | 14.211 | 1.286 | 9.752 | 2 | −0.154 |

3 | 0.070 | 0.204 | −0.092 | 0.221 | −1.344 | 3 | 25.63 |

4 | 28.311 | −15.42 | −5.464 | −13.37 | −20.03 | 4 | −0.108 |

5 | −2.917 | 2.188 | −2.978 | 0.235 | −0.657 | 5 | −0.270 |

6 | 3.043 | 1.473 | 2.946 | 2.971 | 1.648 | 6 | 0.292 |

7 | −0.374 | 1.921 | 1.376 | 2.425 | −3.305 | 7 | −0.758 |

Bias | 21.27 |

W1 Neuron | [Ag-TiO_{2}]_{0} | [AY23]_{0} | UV light | Time | Bias | W2 Neuron | Weight |
---|---|---|---|---|---|---|---|

2 | −0.258 | −0.824 | 0.999 | −0.712 | −0.979 | 2 | 0.341 |

3 | 0.003 | −0.864 | −0.483 | 0.726 | 0.775 | 3 | 0.382 |

4 | −0.799 | −0.347 | 0.081 | −0.596 | −0.043 | 4 | -0.879 |

5 | −0.651 | −0.210 | −0.667 | 0.189 | −0.466 | 5 | −0.514 |

6 | −0.448 | 0.465 | −0.994 | −0.369 | −0.161 | 6 | −0.899 |

Bias | −0.803 |

Input Variable | Importance (%) |
---|---|

Ag-TiO_{2} initial dosage (g/L) | 10 |

AY23 initial concentration (mg/L) | 40 |

UV light intensity (W/m^{2}) | 30 |

Time (min) | 20 |

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**MDPI and ACS Style**

Razvarz, S.; Jafari, R.
ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater. *Math. Comput. Appl.* **2017**, *22*, 38.
https://doi.org/10.3390/mca22030038

**AMA Style**

Razvarz S, Jafari R.
ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater. *Mathematical and Computational Applications*. 2017; 22(3):38.
https://doi.org/10.3390/mca22030038

**Chicago/Turabian Style**

Razvarz, Sina, and Raheleh Jafari.
2017. "ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater" *Mathematical and Computational Applications* 22, no. 3: 38.
https://doi.org/10.3390/mca22030038