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The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach

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TLDR
The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4 for the feed-forward back-propagation and layer recurrent neural network model.
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This article is published in Water Science and Technology.The article was published on 2017-11-16 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Mean absolute percentage error.

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Deep Eutectic Solvents: A Review of Fundamentals and Applications.

TL;DR: A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in deep eutectic solvents, and highlights recent research efforts to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding.
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Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research

TL;DR: In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly and several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.
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Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

TL;DR: This review summarizes various AI techniques and their applications in water treatment with a focus on the adsorption of pollutants and makes recommendations to ensure the successful applications of AI in future water-related technologies.
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Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

TL;DR: A comprehensive overview of AI applications in water treatment is presented in this article , where the potential of AI in predicting the performances of adsorption processes are portrayed in detail and the major challenges in AI applications are accentuated.
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Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents

TL;DR: A general guideline for the selection of a suitable modelling approach for process development and simulation is presented based on the framework of the application and challenges, perspectives and future way forward are provided.
References
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Journal ArticleDOI

Biosorption of heavy metals by Saccharomyces cerevisiae: a review.

TL;DR: The state of the art in the field of biosorption of heavy metals by S. cerevisiae not only in China, but also worldwide, is reviewed in this paper, based on a substantial number of relevant references published recently on the background of biosOrption achievements and development.
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Heavy metal removal from aqueous solution by advanced carbon nanotubes: Critical review of adsorption applications

TL;DR: In this article, the use of carbon nanotubes (CNTs), member of the fullerene structural family, is considered with special focus on the removal of heavy metals from water (lead, chromium, cadmium, arsenic, copper, zinc and nickel).
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Synthesis and characterization of alumina-coated carbon nanotubes and their application for lead removal

TL;DR: Alumina-coated multi-wall carbon nanotubes were synthesized and characterized and displayed the main advantage of separation convenience when a fixed-bed column was used compared to the batch adsorption treatment.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan

TL;DR: In this article, the authors compared ANN-GA and Response Surface Methodology (RSM) for fermentation media optimization, and found that the ANN algorithm outperformed RSM in terms of sensitivity analysis and optimization ability.
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Glycerol-based deep eutectic solvents: Physical properties

TL;DR: In this paper, 70 DESs were synthesized successfully based on glycerol (Gly) as the HBD with different phosphonium and ammonium salts, namely methyl triphenyl phosphono-bromide (MTPB), benzyl triphenyi-triphenyl-phosphonium bromide(BTPC), allyl triphethenyl phono-phonium (ATPB), choline chloride (ChCl), N,N-diethylethanolammonium chloride (DAC), and tetra-
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