Artificial Neural Network-Genetic Algorithm Prediction of Heavy Metal Removal Using a Novel Plant-Based Biosorbent Banana Floret: Kinetic, Equilibrium, Thermodynamics and Desorption Studies

Sutherland, Clint; Marcano, Abeni; Chittoo, Beverly S.

doi:10.5772/intechopen.74398

Cited by 4 publications

(1 citation statement)

References 43 publications

(36 reference statements)

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“…A GA work is an optimization heuristic. It aims at identifying the optimal solution from the feasible solution area using a fitness (objective) function and its limitation [25,26]. A GA can follow two major directions: focusing on the optimization of single objective function or the optimization of multi-objective functions.…”

Section: Genetic Algorithm (Ga) With Ann and Rsm Comparisonmentioning

confidence: 99%

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

et al. 2023

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Lead (Pb) is well known for the containment of soil surfaces. In the last few decades, phytoremediation has been the most ideal technology to extract Pb from soil, involving numerous chemical reactions and cost analysis. The aim of this study is to model and to optimize Pb extraction from the contaminated soil via Pelargonium hortorum by comparing two modeling approaches: response surface methodology (RSM) and artificial neural networks (ANNs) with the genetic algorithm (GA). To determine the significance of the proposed solution, in vitro essays were performed to check the Pb tolerance of bacterial strains (NCCP 1844, 1848, 1857, and 1862), followed by the co-application of bacteria and citric acid on a Pb hyperaccumulator (Pelargonium hortorum L.) on Murashige and Skoog (MS) agar medium. Afterwards, a pot culture experiment was performed to optimize Pb extraction competency from Pb-spiked (0 mg kg−1, 500 mg kg−1, 1000 mg kg−1, and 1500 mg kg−1) soil by Pelargonium hortorum L., to which citric acid (5 and 10 mmol L−1) and Microbacterium paraoxydance (1 and 1.5 OD) were applied. Plants were harvested at 30, 60, and 90 day intervals, and they were analyzed for dry biomass and Pb uptake characteristics. The maximum Pb extraction efficiency of 86.0% was achieved with 500 mg kg−1 soil Pb for 60 days. Furthermore, RSM, based on the Box–Behnken design (BBD) and the ANN-based Levenberg–Marquardt Algorithm (LMA), were applied to model Pb extraction from the soil. The significance of the predicted values from RSM and LMA were close to 36.0% and 86.05%, respectively, compared to the laboratory values. The comprehensive evaluation of these findings encouraged the accuracy, reliability, and efficiency of the ANN for the optimization process. Therefore, experimental results showed that ANN is an accurate technique to optimize an integrated phytoremediation system for sustainable Pb removal, besides being environmentally friendly and potentially cost-effective.

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Section: Genetic Algorithm (Ga) With Ann and Rsm Comparisonmentioning

confidence: 99%

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

et al. 2023

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Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs)

Nighojkar

Plappally

Soboyejo

2023

Neural Comput & Applic

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Application of ANN Predictive Model for the Design of Batch Adsorbers - Equilibrium Simulation of Cr(VI) Adsorption onto Activated Carbon

Sutherland¹,

Chittoo²,

Venkobachar³

2019

TOCIEJ

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Background: Escalation of industrial processes continues to increase the concentrations of Cr(VI) in wastewater above permissible discharge limits. Persistent exposure to Cr(VI)may result in deleterious effects on human health, aquatic life, and the environment. Laboratory-scale adsorption studies have proven effective in achieving the low treatment levels demanded by statutory authorities. The eventual design of the pilot and full-scale systems hinges on the ability to predict adsorption behavior mathematically. Objective: The objective of this study is to elucidate the mechanism of Cr(VI) adsorption and to develop an Artificial Neural Network (ANN) model capable of accurately simulating complex multi-layered adsorption processes. Methods: Batch equilibrium experiments were conducted for the removal of Cr(VI) by activated carbon. Conventional two and three-parameter equilibrium models such as the Langmuir, Freundlich, Sips, original BET and modified BET were used to simulate the data and expound the mechanism of adsorption. An ANN model was constructed with the built-in effect of the residual Cr(VI) concentration for the prediction of the equilibrium sorption capacity. Results: The modified BET model was most successful at predicting the monolayer coverage. However, the model failed to capture the complex shape of the isotherm at higher initial concentrations. The highest correlation to the equilibrium data was revealed by the ANN model (R2 = 0.9984). Conclusion: A batch adsorber was successfully designed using mass balance, and incorporating the predictive ability of the ANN model. In spite of the ANN’s ability to simulate the adsorption process, it provides little insight into the mechanism of adsorption. However, its ability to accurately predict Cr(VI) removal enables the up-scaling of the adsorption processes to pilot and full-scale design.

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Artificial Neural Network-Genetic Algorithm Prediction of Heavy Metal Removal Using a Novel Plant-Based Biosorbent Banana Floret: Kinetic, Equilibrium, Thermodynamics and Desorption Studies

Cited by 4 publications

References 43 publications

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs)

Application of ANN Predictive Model for the Design of Batch Adsorbers - Equilibrium Simulation of Cr(VI) Adsorption onto Activated Carbon

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