Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

Zhang, Wentao; Huang, Wenguang; Tan, Jie; Huang, Dawei; Ma, Jun; Wu, Bingdang

doi:10.1016/j.chemosphere.2022.137044

Cited by 47 publications

(20 citation statements)

References 138 publications

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“…Today, it continues to play a leading role in correlating the adsorption data of micropollutants or contaminants of emerging concern (pesticides, pharmaceuticals, and personal care products). , However, data generation in the laboratory is time-consuming, requiring a substantial number of experiments to cover a wide range of concentrations. A potential solution can be to predict the two Freundlich isotherm parameters using advanced techniques such as machine learning algorithms . Some commonly used machine learning algorithms include artificial neural networks, support vector machines, and random forest.…”

Section: Discussionmentioning

confidence: 99%

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

et al. 2023

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The name of Herbert Freundlich is commonly associated with a power relationship for adsorbed amount of a substance (C ads ) against the concentration in solution (C sln ), such that C ads = KC sln n ; this isotherm (together with the Langmuir isotherm) is considered to be the model of choice for correlating the experimental adsorption data of micropollutants or contaminants of emerging concern (pesticides, pharmaceuticals, and personal care products), but it also concerns the adsorption of gases on solids. However, Freundlich's 1907 paper was a "sleeping beauty", which only started to attract significant citations from the early 2000s onward; moreover, these citations were too often wrong. In this paper, the main steps in the historical developments of Freundlich isotherm are identified, along with a discussion of several theoretical points: (1) derivation of the Freundlich isotherm from an exponential distribution of energies, leading to a more general equation, based on the Gauss hypergeometric function, of which the power Freundlich equation is an approximation; (2) application of this hypergeometric isotherm to the case of competitive adsorption, when the binding energies are perfectly correlated; and (3) new equations for estimating the Freundlich coefficient K F from physicochemical properties such as the sticking surface or probability. From new data treatment of two examples from the literature, the influence of several parameters is highlighted, and the application of linear free-energy relationships (LFER) to the Freundlich parameters for different series of compounds is evoked, along with its limitations. We also suggest some ideas that may be worth exploring in the future, such as extending the range of applications of the Freundlich isotherm by means of its hypergeometric version, extending the competitive adsorption isotherm in the case of partial correlation, and exploring the interest of the sticking surfaces or probabilities instead of K F for LFER analysis.

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Section: Discussionmentioning

confidence: 99%

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

et al. 2023

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“…2a. Feature engineering refers to selecting appropriate descriptors as model inputs to accurately predict model outputs (Zhang et al 2022b(Zhang et al , 2022c. The employed descriptors usually include pyrolysis conditions, adsorption conditions, and physicochemical properties of biochar (Fig.…”

Section: General Concepts Of Machine Learningmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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“…The adsorption method is an effective and widely used technique for removing drug pollutants from wastewater because of its high efficiency, non-toxicity, ease of operation, and cost-effectiveness. − Using different types of materials to adsorb pollutants from wastewater offers versatility and eco-friendliness for treating polluted water. − Mixed matrix membranes (MMMs) are composite membranes that combine organic and inorganic materials, resulting in improved performance compared to conventional membranes. − …”

Section: Introductionmentioning

confidence: 99%

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO₃-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study

Salahshoori,

Montazeri,

Yazdanbakhsh

et al. 2023

ACS Appl. Mater. Interfaces

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The effect of the COVID-19 pandemic on the accumulation of environmental pollutants has been significant. In that way, waste management systems have faced problems, and the amount of hazardous and medical wastes has increased. As pharmaceuticals associated with the treatment of COVID-19 enter the environment, aquatic and terrestrial ecosystems have been negatively impacted, potentially disrupting natural processes and harming aquatic life. This analysis seeks to appraise the potential of mixed matrix membranes (MMMs) composed of Pebax 1657-g-chitosan-polyvinylidene fluoride (PEX-g-CHS-PVDF)-bovine serum albumin (BSA)@ZIF-CO 3 -1 as adsorbents for removing remdesivir (REMD) and nirmatrelvir (NIRM) from aqueous environments. An in silico study was conducted to explore the adsorption characteristics, physicochemical properties, and structural features of these MMMs, employing quantum mechanical (QM) calculations, molecular dynamics (MD) simulations, and Monte Carlo (MC) simulations as research methodologies. Incorporating BSA@ZIF-CO 3 -1 into the PEX-g-CHS-PVDF polymer matrix improved the physicochemical properties of MMMs by promoting the compatibility and interfacial adhesion between the two materials, facilitated by electrostatic interactions, van der Waals forces, and hydrogen bonding. Investigation of the interaction mechanism between the title pharmaceutical pollutants and the surfaces of MMMs, along with the description of their adsorption behavior, was also conducted by applying MD and MC approaches. Our observations indicate that the adsorption behavior of REMD and NIRM is influenced by molecular size, shape, and the presence of functional groups. Molecular simulation analysis demonstrated that the MMM membrane is a highly suitable adsorbent for the adsorption of REMD and NIRM drugs, with a higher affinity toward REMD adsorption. Our study emphasizes the significance of computational modeling in developing practical strategies for eliminating COVID-19 drug contaminants from wastewater. The knowledge obtained through our molecular simulations and QM calculations can assist in creating more efficient adsorption materials, resulting in a cleaner and healthier environment.

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Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

Cited by 47 publications

References 138 publications

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO₃-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study

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Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

Cited by 47 publications

References 138 publications

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO3-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study

Contact Info

Product

Resources

About

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO₃-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study