Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar, Amrita; Zimmermann, Karl; Ateia, Mohamed; Barbeau, Benoît; Mohseni, Madjid; Krishnamurthy, S.; Dixit, Fuhar; Kandasubramanian, Balasubramanian

doi:10.1039/d2va00200k

Cited by 18 publications

(8 citation statements)

References 207 publications

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“…The application of ML and DL aligns seamlessly with the global shift towards sustainable practices [61]. The remarkable ability of these techniques to extract insights from complex datasets positions researchers at the forefront of innovation, fostering a culture of continuous improvement and adaptive strategies in the pursuit of sustainable environmental solutions [46].…”

Section: Shap Analysismentioning

confidence: 99%

“…However, most of these methods have primarily focused on predicting removal efficiency and adsorption capacity in equilibrium studies, with limited emphasis on modeling continuous and batch adsorption kinetics. Notably, the complex kinetic removal of Cr(VI) using these biomaterials, considering both Cr(VI) and Cr(T), has not been previously modeled using ML or DL techniques [46,47]. Understanding this kinetic complexity is crucial for accurately assessing the material's efficiency in removing the different chromium species and optimizing process conditions.…”

Section: Introductionmentioning

confidence: 99%

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Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark

Cruz-Victoria,

Netzahuatl-Muñoz,

Cristiani-Urbina

2024

Sustainability

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Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications.

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Section: Shap Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark

Cruz-Victoria,

Netzahuatl-Muñoz,

Cristiani-Urbina

2024

Sustainability

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“…Recently with the evolution in machine learning (ML) approaches, many have explored the area of AI-driven processes for understanding and predicting new materials and their properties [103]. In this domain, first principle calculations based on evaluating the energies by means of Schrodinger equation are often used to analyse structure and other properties.…”

Section: Examining Mxene Structural Fundamentals With Artificial Inte...mentioning

confidence: 99%

Thinking green with 2-D and 3-D MXenes: Environment friendly synthesis and industrial scale applications and global impact

Saxena

Johnson

Dixit

et al. 2023

Renewable and Sustainable Energy Reviews

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“…In comparison among all the three methods, ANN models have given a considerable prediction of influencing parameters on the required corrosion properties. Nighojkar et al (2022) have studied the importance of metal adsorption through biomaterials in water contaminant systems. To improve the adsorption behavior of the biomaterials, surface modifications and physical changes were required.…”

Section: Surface Treatment Of Biomaterials and Implantsmentioning

confidence: 99%

Computational intelligence based design of biomaterials

Arulraj¹,

Datta²

2022

cmms

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This paper presents an overview of the applications of computational intelligence techniques, viz. artificial neural networks, fuzzy inference systems, and genetic algorithms, for the design of biomaterials with improved performance. These techniques are basically used for developing data-driven models and for optimization. The paper introduces the domain of biomaterials and how they can be designed using computational intelligence techniques. Then a brief description of the tools is made, followed by the applications of the tools in various domains of biomaterials. The applications range in all classes of materials ranging from alloys to composites. There are examples of applications for the surface treatment of biomaterials, materials for drug delivery systems, materials for scaffolds and even in implant design. It is found the tools can be effectively used for designing new and improved biomaterials.

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Application of neural network in metal adsorption using biomaterials (BMs): a review

Abstract: ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.

Cited by 18 publications

References 207 publications

Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark

Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark

Thinking green with 2-D and 3-D MXenes: Environment friendly synthesis and industrial scale applications and global impact

Computational intelligence based design of biomaterials

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