Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm

Mohammadi, Farzaneh; Samaei, Mohammad Reza; Azhdarpoor, Abooalfazl; Teiri, Hakimeh; Badeenezhad, Ahmad; Rostami, Saeid

doi:10.1016/j.chemosphere.2019.124486

Cited by 65 publications

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, the training was used to determine the weight and bias, thus controlling the validation error rate. Then, the training was stopped to avoid an over-tting increase when the validation error was increased by special iterations (20). Given the signi cance and accuracy of the mean square error (MSE) and correlation coe cient (R), the best ANN structure for predicting cell proliferation in MRC-5 exposed to SiO 2 NPs, based on MSE and R from the number of neurons, in the hidden layer recommended.…”

Section: Ann Modellingmentioning

confidence: 99%

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

Abbasi

Hashemi

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: This study investigated the effect of the level of silica nanoparticles (SiO2NPs) crystallization on the cell proliferation of MRC-5 cells and its prediction using an artificial neural network (ANN).Methods: Variables studied included temperature (70-1000°C), calcination time (2, 12 and 24 hours), and catalyst feed rate (0.01, 0.05 and 0.1mL/min). Cell proliferation was determined by the MTT test after 24 hours of exposure, and results were analyzed using the t-test in MATLAB.Results: the synthesized particles size was less than 50nm, and the XRD peak varied from 30 to 21° during the increase in calcination temperature. The maximum level of crystallization was at 800°C (58% relative to amorphous) with the lowest cell viability. Cell proliferation decreased with increasing concentration of nanoparticles (p<0.05) and increasing feed rate. There was also a positive relationship between increased crystallization and decreased cell proliferation (R2=0.78), but no such association was observed for calcination time. Cell proliferation of MRC-5 was slightly correlated with the linear regression model (MSE>0.12), while ANN was well predicted by the Levenberg–Marquardt algorithm. The suggested structure in this study was 4:10:1 with R2all=0.97, R2test=0.97, RMSE=0.25 and MSE=0.003. The correlation between laboratory results and ANN prediction was 0.94, and the minimum and maximum OD level in the laboratory data and predicted ANN were attributed to 20 and 13 runs.Conclusion: changes in the degree of crystallization of SiO2NPs, an increase in concentration, and the rate of catalyst feed during crystallization of SiO2NPs were practical factors in increasing cytotoxicity.

show abstract

Section: Ann Modellingmentioning

confidence: 99%

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

Abbasi

Hashemi

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…While, the mutation operator slightly modifies the parental string so as to diversify the generation within a population 27 . The GA process went on continuously until it approached to an approximate optimum solution 28 . The GA operations reiterated several times by varying the input space parameters until the accomplishment of suitable results 19 .…”

Section: Methodsmentioning

confidence: 99%

ANN-GA based biosorption of As(III) from water through chemo-tailored and iron impregnated fungal biofilter system

Tripathi

Ranjan

Verma

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The iron impregnated fungal bio-filter (IIFB) discs of luffa sponge containing Phanerochaete chrysosporium mycelia have been used for the removal of As(III) from water. Two different forms of same biomass viz. free fungal biomass (FFB) and modified free fungal biomass (chemically modified and iron impregnated; CFB and IIFB) have been simultaneously investigated to compare the performance of immobilization, chemo-tailoring and iron impregnation for remediation of As(III). IIFB showed highest uptake capacity and percentage removal of As(III), 1.32 mg/g and 92.4% respectively among FFB, CFB and IIFB. Further, the application of RSM and ANN-GA based mathematical model showed a substantial increase in removal i.e. 99.2% of As(III) was filtered out from water at optimised conditions i.e. biomass dose 0.72 g/L, pH 7.31, temperature 42 °C, and initial As(III) concentration 1.1 mg/L. Isotherm, kinetic and thermodynamic studies proved that the process followed monolayer sorption pattern in spontaneous and endothermic way through pseudo-second order kinetic pathway. Continuous mode of As(III) removal in IIFB packed bed bioreactor, revealed increased removal of As(III) from 76.40 to 88.23% with increased column height from 5 to 25 cm whereas the removal decreased from 88.23 to 69.45% while increasing flow rate from 1.66 to 8.30 mL/min. Moreover, the IIFB discs was regenerated by using 10% NaOH as eluting agent and evaluated for As(III) removal for four sorption–desorption cycles, showing slight decrease of their efficiency by 1–2%. SEM–EDX, pHzpc, and FTIR analysis, revealed the involvement of hydroxyl and amino surface groups following a non-electrostatic legend exchange sorption mechanism during removal of As(III).

show abstract

“…The feed-forward back propagation neural network is a popular ANN model that is used for many engineering applications [37,38,39]. The feed-forward back propagation network contains three types of layers; the color information from three components was received on the neurons represented by circles which are called the input layer and an output layer having a single neuron and giving the interior calculation outcome.…”

Section: Ann Structurementioning

confidence: 99%

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Al-Naji

Fakhri

Gharghan

et al. 2021

Heliyon

View full text Add to dashboard Cite

Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10 −6 (training), 1.004 × 10 −5 (testing) and 1.809 × 10 −5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.

show abstract

Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm

Cited by 65 publications

References 36 publications

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

ANN-GA based biosorption of As(III) from water through chemo-tailored and iron impregnated fungal biofilter system

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Contact Info

Product

Resources

About