The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii

Podder, M.S.; Majumder, C. B.

doi:10.1016/j.saa.2015.11.011

Cited by 45 publications

(19 citation statements)

References 52 publications

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“…and A. niger , respectively, at seven days (Figure 7). The ability to remove by biomass is equal to or greater than that by other biomasses that have been studied, for different heavy metals, like C. albicans biomass to remove chromium(VI) from sediments and effluents, in which 74 and 69% of metal present in the contaminated water and soil were removed [10], and Botryococcus braunii biomass to remove As(III) and As(V) ions from the 50 mg/L synthetic wastewater, in which 85.22% and 88.15% of maximum removal efficiency were achieved [36]. Also, C. tropicalis was observed to remove 40% Cd(II) from the wastewater after 6 days and was also able to remove 78% from the wastewater after 12 days [37]; different species of the genus Aspergillus have the capacity to remove approximately between 20 and 50% of 100 mg/L of Hg(II) using 1 g of biomass, with a temperature of 30°C and a pH of 5.5; these data are lower than those reported in this research because mercury is more toxic and causes the inhibition of cellular glucose uptake and then cellular respiration, and therefore, there is no growth of microorganisms [38].…”

Section: Resultsmentioning

confidence: 99%

Bioremoval of Cobalt(II) from Aqueous Solution by Three Different and Resistant Fungal Biomasses

González

Pérez

Morales

et al. 2019

Bioinorganic Chemistry and Applications

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The biosorption of Co(II) on three fungal biomasses: Paecilomyces sp., Penicillium sp., and Aspergillus niger, was studied in this work. The fungal biomass of Paecilomyces sp. showed the best results, since it removes 93% at 24 h of incubation, while the biomasses of Penicillium sp. and Aspergillus niger are less efficient, since they remove the metal 77.5% and 70%, respectively, in the same time of incubation, with an optimum pH of removal for the three analyzed biomasses of 5.0 ± 0.2 at 28°C. Regarding the temperature of incubation, the most efficient biomass was that of Paecilomyces sp., since it removes 100%, at 50°C, while the biomasses of Penicillium sp. and Aspergillus niger remove 97.1% and 94.1%, at the same temperature, in 24 hours of incubation. On the contrary, if the concentration of the metal is increased, the removal capacity for the three analyzed biomasses decreases; if the concentration of the bioadsorbent is increased, the removal of the metal also increases. It was observed that, after 4 and 7 days of incubation, 100%, 100%, and 96.4% of Co(II) present in naturally contaminated water were removed, respectively.

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Section: Resultsmentioning

confidence: 99%

Bioremoval of Cobalt(II) from Aqueous Solution by Three Different and Resistant Fungal Biomasses

González

Pérez

Morales

et al. 2019

Bioinorganic Chemistry and Applications

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show abstract

“…To resolve this issue, a data augmentation technique is necessary for increasing the number of data points in the training set. Earlier, Podder et al [47] used a cubic spline function for generating interpolated data points for their ANN-based modeling of As adsorption efficiency. Cubic spline or piecewise cubic interpolation can be categorized as an exact point interpolation method in the family of spatial interpolation techniques.…”

Section: Data Interpolationmentioning

confidence: 99%

“…This piecewise polynomial interpolation method, unlike its polynomial analogs, is capable of finding a continuous second derivative at all data points by minimizing the interpolation errors and produces a smoother distribution of interpolated data points within a certain range [48,49]. The cubic spline interpolation also mitigates the distortion issues in boundary regions observed in least-squares interpolation [47]. Considering the advantages, a piecewise cubic interpolation method was adopted to interpolate the original data points in the current study.…”

Section: Data Interpolationmentioning

confidence: 99%

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Hafsa

Rushd

Al-Yaari

et al. 2020

Water

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Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.

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“…However, mathematical modelling (process simulations and predictions) has been suggested as a means to optimize and control the performance of an AD process (Revilla et al, 2016) could learn the complex and non-linear relationships existing in an AD process. Developed math models such as neural networks were successful when employed to capture the nonlinear relationships existing in AD process (Podder & Majumder, 2016;Antwi et al, 2017c). ANN have been trained to perform complex functions in various fields of application including pattern recognition, identification, classification, speech, vision, and control systems.…”

Section: Introductionmentioning

confidence: 99%

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

Antwi

Meng

et al. 2018

Bioresource Technology

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In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R), the BPANN model demonstrated significant performance with R reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.

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The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii

Cited by 45 publications

References 52 publications

Bioremoval of Cobalt(II) from Aqueous Solution by Three Different and Resistant Fungal Biomasses

Bioremoval of Cobalt(II) from Aqueous Solution by Three Different and Resistant Fungal Biomasses

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

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