Optimization on cleaner intensification of ozone production using Artificial Neural Network and Response Surface Methodology: Parametric and comparative study

Hafeez, Ainy; Taqvi, Syed Ali Ammar; Fazal, Tahir; Javed, Fahed; Khan, Zakir; Amjad, Um-e-Salma; Bokhari, Awais; Shehzad, Nasir; Rashid, Naim; Rehman, Saifur; Rehman, Fahad

doi:10.1016/j.jclepro.2019.119833

Cited by 73 publications

(16 citation statements)

References 46 publications

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“…The number of hidden layers and hidden neurons of the trained ANN model substantially impacts its predictive performance. Therefore, carefully choosing the number of hidden layers and hidden neurons is critical [ 43 ]. MATLAB ® 2020b was utilized in this research to develop the FFBPN-based ANN model to predict the response.…”

Section: Methodsmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Irfan

Waqas

Arshad³

et al. 2022

Materials

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Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.

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Section: Methodsmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Irfan

Waqas

Arshad³

et al. 2022

Materials

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“…The ANN has been developed for a long time (Klemeš and Ponton, 1992) with many variations (Ponton and Klemeš, 1993;Shahbaz et al, 2020;Rashid et al, 2021). The FFBPN model has been widely used in the literature owing to its good pattern classifier and overall efficiency capability (Hafeez et al, 2020;Arshad et al, 2021). The proposed feed-forward network is a supervised learning technique comprised of three-layered architecture for data prediction, that is, the input, the hidden, and the output layers [ (Naqvi et al, 2019a;Naqvi et al, 2018b.…”

Section: Predictive Model Development Using Feed Forward Neural Networkmentioning

confidence: 99%

Valorization of Wet Oily Petrochemical Sludge via Slow Pyrolysis: Thermo-Kinetics Assessment and Artificial Neural Network Modeling

et al. 2022

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Oily sludge is a hazardous waste stream of oil refineries that requires an effective process and an environment-friendly route to convert and recover valuable products. In this study, the pyrolytic conversion of the wet waste oil sludge was implemented in an autoclave pyrolyzer and a thermogravimetric analyzer (TGA) at 5°C/min, 20°C/min, and 40°C/min, respectively. The kinetic analysis was performed using model-free methods, such as Friedman, Kissenger–Akahira–Sunose (KAS), and Ozawa–Flynn–Wall (OFW) to examine the complex reaction mechanism. The average activation energy of wet waste oil sludge (WWOS) estimated from Friedman, KAS, and OFW methods was 198.68 ± 66.27 kJ/mol, 194.60 ± 56.99 kJ/mol, and 193.28 ± 54.88 kJ/mol, respectively. The activation energy increased with the conversion, indicating that complex multi-step processes are involved in the thermal degradation of WWOS. An artificial neural network (ANN) was employed to predict the conversion during heating at various heating rates. ANN allows complex non-linear relationships between the response variable and its predictors. nH, ΔG, and ΔS were found to be 191.26 ± 2.82 kJ/mol, 240.79 ± 2.82 kJ/mol, and −9.67 J/mol K, respectively. The positive values of ΔH and ΔG and the slightly negative value of ΔS indicate the endothermic nature of the conversion process, which is non-spontaneous without the supply of energy.

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“…It is therefore a polynomial that can be assigned for both linear and non-linear relations (as a function of N ), valid for the three materials: silicone, porcelain, and heat tempered glass, according to the variable under study and the degree of its non-linearity. In addition, it is simple and results in an optimization problem that is easy to solve [22,23].…”

Section: 1 General Model Of Maximum Discharge Currentmentioning

confidence: 99%

Experimental Investigation of the Triple-Junction Effect on the Electric Discharge Characteristics and Modeling of the Maximum Discharge Current

Saim

Bitam-Megherbi

2021

ECTI-EEC

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The electrical discharge and breakdown characteristics of a triple junction (TJ) in high voltage (HV) electrical insulation are becoming an important issue in recent industrial applications. In this paper, a comparative study is conducted between three insulation materials used in HV: silicone, porcelain, and heat tempered glass, with electrode shape parameters such as inter-electrode distance (d), applied voltage (V), and geometry investigated for positive polarity, using an experimental approach. For optimum test organization to support our research and highlight the parameters and their interactions, we have established a mathematical regression model of the discharge current. It is polynomial, with good performance and a high level of precision. Besides, this yields one monovariable and another two-variable quadratic model. The latter can simulate the electric discharge simultaneously with two variables: V and d, and an adjusted determination coefficient () of nearly 0.99. Consequently, the two-variable quadratic model has been adapted to V and d ranges of 10 to 50 kV and 1.9 to 8 cm, respectively. Finally, this study provides us with new applicable knowledge about the TJ effect on electrical insulation with recommendations to optimize the design for higher performance.

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Optimization on cleaner intensification of ozone production using Artificial Neural Network and Response Surface Methodology: Parametric and comparative study

Cited by 73 publications

References 46 publications

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Valorization of Wet Oily Petrochemical Sludge via Slow Pyrolysis: Thermo-Kinetics Assessment and Artificial Neural Network Modeling

Experimental Investigation of the Triple-Junction Effect on the Electric Discharge Characteristics and Modeling of the Maximum Discharge Current

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