Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor

Abstract

An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe(2+) and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

“…although the available information is presented in different forms and usually the resulting accuracies are not referred to the accuracy of the raw information, common CC results are in the range [0.84 -0.95] (Grbic et al, 2013;Jin et al, 2015), and values of RMSE of 3.35 (Mota et al, 2014) and average relative errors of 15% (Duran et al, 2006) are reported.…”

“…A different class of data-based modeling studies has been focused on the prediction of the contaminant degradation dynamics during the treatment time (Göb et al, 1999;Salari et al, 2005;Elmolla et al, 2010;Ayodele et al, 2012;Mota et al, 2014;Mustafa et al, 2014;Gazi et al, 2017;Sebti et al, 2017). To do so, the degradation evolution is assumed to be a function of the initial experiment parameters (e.g.…”

Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network

“…As an alternative to physical models, an ANN is a valuable forecast tool in environmental sciences [24]. The ANN can be used effectively due to its learning capabilities and its low computational costs [25]. Because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables (multi-input/output) in multivariate systems, numerous applications of ANN-based models have been successfully utilized in the field of environmental engineering in the past decade [26][27][28].…”

Analysis of solar and artificial UVA irradiations on the photo-Fenton treatment of phenolic effluent and oilfield produced water

“…These compounds are more difficult to remove from water by using conventional treatment techniques. Therefore, it is necessary to apply more advanced techniques that allow the degradation of these compounds (Yavuz and Koparal, 2006;Saien and Nejati, 2007;Mota et al, 2014). One of the alternatives is advanced oxididation processes (AOPs).…”