Artificial neural network modeling of a deflector in a grooved channel as well as optimization of its effective parameters

Abdollahi, Azita; Shams, Mehrzad; Abdollahi, Anita

doi:10.1007/s00231-017-2076-6

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks are invaluable tools used in numerous complex fields, including image processing, observations, segregation, pattern recognition and control systems [34][35][36][37]. This wide acceptance of ANN approaches over classical simulation techniques comes from their rapid but also simple processing nature as well the capability to learn from historical and/or available examples.…”

Section: Ann Simulationmentioning

confidence: 99%

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Khoshdast¹,

Gholami

Hassanzadeh

et al. 2021

Materials

View full text Add to dashboard Cite

This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60×5.70×0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (p-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec−1. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process.

show abstract

Section: Ann Simulationmentioning

confidence: 99%

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Khoshdast¹,

Gholami

Hassanzadeh

et al. 2021

Materials

View full text Add to dashboard Cite

show abstract

“…In order to find out how the outputs and inputs of a system are related, an ANN can be applied. An ANN consists of the neurons, the computational blocks which are similar to biological brain cells and are simple units building the layers that are related and their computational relations lead to determination of network performance [12][13][14]. A set of neurons is organized in a layer and their outputs are weighed and utilized as input for the forward layer.…”

Section: Ann Modelingmentioning

confidence: 99%

Mathematical modeling of thermal contact resistance for different curvature contacting geometry using robust approach

Shojaeefard

Aghvami

2018

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…Abdollahi et al investigated the variations of HT coefficient and pressure drop (PD) relevant to a channel fluid flow with internal grooves and curved deflectors installed on its surface. The ANN technique was applied for configuration of the deflectors to optimize the PD and HT rate [40]. In another study, the effect of 6 porous baffles in a shell and tube heat exchanger on the HT and PD was numerically analyzed by Mohammadi et al with the aim of ANN.…”

mentioning

confidence: 99%

A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Alizadeh

Abad

Ameri

et al. 2021

Journal of the Taiwan Institute of Chemical Engineers

141

View full text Add to dashboard Cite

Artificial neural network modeling of a deflector in a grooved channel as well as optimization of its effective parameters

Cited by 7 publications

References 17 publications

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Mathematical modeling of thermal contact resistance for different curvature contacting geometry using robust approach

A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Contact Info

Product

Resources

About