<b>A bootstrapped neural network model applied to prediction of the biodegradation rate of reactive Black 5 dye</b> - doi: 10.4025/actascitechnol.v35i3.16210

Abstract

ABSTRACT. Current essay forwards a biodegradation model of a dye, used in the textile industry, based on a neural network propped by bootstrap remodeling. Bootstrapped neural network is set to generate estimates that are close to results obtained in an intrinsic experience in which a chemical process is applied. Pseudomonas oleovorans was used in the biodegradation of reactive Black 5. Results show a brief comparison between the information estimated by the proposed approach and the experimental data, with a coefficient of correlation between real and predicted values for a more than 0.99 biodegradation rate. Dye concentration and the solution's pH failed to interfere in biodegradation index rates. A value above 90% of dye biodegradation was achieved between 1.000 and 1.841 mL 10 mL -1 of microorganism concentration and between 1.000 and 2.000 g 100 mL -1 of glucose concentration within the experimental conditions under analysis.

Simulación del proceso de biodegradación de aguas residuales de la industria de carne mediante una red neuronal artificial perceptrón multicapa

“…Es importante destacar que las mayores diferencias que se muestran en las Figuras 2a, 2b y 3b entre 40 h y 50 h, puede indicar un problema en el proceso de recopilación de los datos. En el caso de la simulación ilustrada en la Figura 4, se puede determinar, por ejemplo, que el momento perfecto del proceso fue el de 35 h. Esto demuestra que este proceso es más eficiente que el tratamiento microbiológico, ya que de acuerdo con [22] estos procesos llevan semanas para degradar las proteínas de todos los efluentes.…”

“…En la literatura reciente, existen varios trabajos que proponen el uso de redes neuronales artificiales en la optimización de los parámetros en los procesos de biodegradación de aguas residuales, especialmente en los sectores textil, metalúrgico, químico y farmacéutico. Entre ellas podemos mencionar [19][20][21][22][23][24][25].…”

Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo

“…The MLP was configured with the same 6 neurons in the input layer and 1 neuron in the output layer, ranging from 3 to 20 neurons in the hidden layer. Functions as logistic, hyperbolic tangent, sine, and exponential were tested for both activation and in the hidden layer and output neuron (Prado et al 2013 ; Rosa et al 2013 ; Santana et al 2015 ).…”

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

“…Application of ANN in the textile industry has also been observed in recent decades, such as: in the prediction of the surface temperature of the fabrics (Bahadir et al, 2019); in heat and moisture propagation in light nonwoven fabrics (Rahnama et al, 2013); in the interaction between hetero cyclic dyestuffs and cellulose (Funar-Timofei et al, 2012); in removing dyes from textile e uents via adsorption onto chitosan-based hybrid hydrogels (Pauletto et al, 2020), by radiolytic degradation (Padmanaban et al, 2018), by biodegradation (Prado et al, 2013;Torbati et al, 2014) or UV/H 2 O 2 , Fenton or photo-Fenton advanced oxidation processes (do Nascimento et al, 2017;Mohajerani et al, 2011;Rosa et al, 2015;Rosa et al, 2020;de Moraes et al, 2021).…”

“…In this sector, reactive dyestuff, which reacts with the bre and water, is used widely for dyeing cellulosic bres. Because of its reaction with water, this dyestuff is a major constituent of e uent; approximately 10-15% of the total dyestuff is disposed as a part of the e uent (Asgari et a., 2020;Lucato et al, 2012;Prado et al, 2013). Therefore, it is important to optimize dyeing processes that use this reactive dyestuff in order to obtain the best coloristic intensity, minimise the salt discharge, and reduce the pollutants in the generated e uent.…”