Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks

Reddy, B.S.; Maurya, A.K.; Narayana, P.L.; Pasha, S. K. Khadheer; Reddy, M.R.; Hatshan, Mohammad Rafe; Darwish, Naif A.; Kori, S.A.; Cho, Kwon-Koo; Reddy, N.S.

doi:10.1016/j.envres.2021.112359

Cited by 13 publications

(7 citation statements)

References 63 publications

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“…The influence of each input variable on the specific capacitance (F/g) for any chosen single data set can be investigated by I RI . The I RI calculation procedure was reported in previous studies, − and the magnitude and direction of I RI indicate the more substantial the relationship between input and output parameters. A negative I RI value indicates an adversarial relationship between the output and input variables.…”

Section: Resultsmentioning

confidence: 99%

Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors

Reddy,

Maurya,

Paturi

et al. 2023

Energy Fuels

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An artificial neural network (ANN) model is developed in this study to predict and analyze the specific capacitance of activated-carbon-based supercapacitors by utilizing a 12-dimensional data set related to physicochemical and operational parameters from the literature. A total of 61 ANN model architectures are constructed using a backpropagation algorithm to estimate the specific capacity. The 12−11−11−11−1 ANN architecture achieves good accuracy, with an average error of 5.8 and adjusted R 2 of 0.99. A standalone ANN software is developed for modeling specific capacity for infinite combinations of physicochemical and operational parameters. The findings illustrate the significance of structural characteristics and pyrolytic and oxidized N groups in determining the supercapacitor performance. Furthermore, the suggested approach can potentially guide future experiments to choose high-performance activated-carbon-based supercapacitor electrodes based on desired specific capacity.

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Section: Resultsmentioning

confidence: 99%

Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors

Reddy,

Maurya,

Paturi

et al. 2023

Energy Fuels

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“…Furthermore, the results indicated that calcined samples in MB photodegration reactions followed the Langmuir-Hinshelwood kinetic model and that the degradation was a pseudo-first order reaction, which, however, most likely ends at shorter UV exposure times. According to the theory of the photocatalytic reaction process, after the reaction is initiated by photons, the final product is desorbed from the surface of the photocatalyst and transferred from the surface to the fluid mass by interfacial diffusion [17].…”

Section: Kinetic Studiesmentioning

confidence: 99%

“…The model is based on biological neural systems with an interconnection of functions and variables that are revealed in three main layers of neurons (input, hidden and output layers) which are adjusted with their weights and biases [15]. Besides the numerous advantages of the ANN model, this model can successfully be employed to simulate the process and define the significance of the various operating variables [16,17].…”

mentioning

confidence: 99%

Photocatalytic Performance of TiO<sub>2</sub>-ZnAl LDH Based Materials: Kinetics and Neural Networks Approach

Hadnadjev-Kostic¹,

Vulic²,

Lukić³

et al. 2022

Pol. J. Environ. Stud.

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Over the past few years, due to large-scale industrialization water pollution has considerably increased in ecosystems and has become a rising aquatic environmental problem. The use of organic dyes in different industries lead to substantial amounts of colored wastewater [1]. It has been stated in the literature that 70% of these dyes impose as complex synthetic, hazardous azo structures and around 12-15% are discharged into the environment untreated causing severe threat to human health and environmental safety [2]. Rhodamine B (RhB) and Methylene Blue (MB) are the most frequently used dyes in paper, plastic, textile industries and can have mutagenic and toxic properties impacting the environment causing serious acute and chronic health issues in living organisms

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“…The robust ML methods can recognize the relationship between the different parameters of the process and their corresponding outcomes through passing the requirement to solve theoretical equations 37 , 38 . The prognostication of nonlinear and complicated systems performance, high reliability, recognizing each influencing parameter's effect, and finding their optimal are some of the ML approaches advantages 39 – 49 .…”

Section: Introductionmentioning

confidence: 99%

“…The prognostication of nonlinear and complicated systems performance, high reliability, recognizing each influencing parameter's effect, and finding their optimal are some of the ML approaches advantages 39 – 49 . Several researchers have applied different ML methods to anticipate the contaminant photocatalytic removal based on different operational variables as inputs 37 – 39 , 50 – 53 .…”

Section: Introductionmentioning

confidence: 99%

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Gheytanzadeh

Baghban

Habibzadeh

et al. 2022

Sci Rep

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Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R2, MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

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Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks

Cited by 13 publications

References 63 publications

Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors

Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors

Photocatalytic Performance of TiO<sub>2</sub>-ZnAl LDH Based Materials: Kinetics and Neural Networks Approach

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

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