From classic to cutting-edge solutions: A comprehensive review of materials and methods for heavy metal removal from water environments

Desalination and Water Treatment, Год журнала: 2024, Номер 319, С. 100446 - 100446

Опубликована: Май 31, 2024

Waterborne heavy metals pose significant threats to both the environment and public health, causing neurological damage, developmental disorders, organ cancer. It is essential address contamination of various water bodies by these hazardous substances due their persistent nature lack biodegradability. This review thoroughly explores traditional modern strategies utilized remedy sources contaminated with evaluates effectiveness potential scalability each method. A literature survey reveals several effective for removing from water: adsorption, flotation, ion exchange, chemical precipitation, membrane-based filtration, coagulation, flocculation, phytoremediation, electrochemical methods. In our examination, we discussed agents/adsorbents used, efficiency removal technique, operating conditions, intrinsic advantages disadvantages approach. Summarizing key findings, noted a focus on adsorption techniques; however, challenges include selectively diverse ions, prolonged retention times, cycling stability. While membrane methods are practical, issues like large-volume sludge formation exist. Although electrical-based appear promising, solutions needed industrial-scale separation as well. Prioritizing real wastewater samples in studies crucial. Future research should emphasize eco-friendly, cost-effective

Язык: Английский

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Strategies for ammonia recovery from wastewater: a review

Environmental Chemistry Letters, Год журнала: 2024, Номер 22(6), С. 2699 - 2751

Опубликована: Авг. 23, 2024

Abstract The circular economy requires advanced methods to recycle waste matter such as ammonia, which can be further used a fuel and precursor of numerous value-added chemicals. Here, we review for the recovery ammonia from wastewater with emphasis on biological physicochemical techniques, their applications. Biological techniques involve nitrification, denitrification, anammox processes use membrane bioreactors. Physicochemical comprise adsorption, filtration, ion exchange, chemical precipitation, stripping, electrochemical oxidation, photocatalytic bioelectrochemical systems, hybrid systems. We found that nitrification in bioreactors stand out cost-effectiveness, reduced sludge production, energy efficiency. struvite precipitation is an efficient, environmentally friendly, recyclable method removal. Membrane systems are promising recovery, nutrient concentration, treatment, applications fertilizer production water purification. Overall, nitrogen removal ranges 28 100%, 9 100%.

Язык: Английский

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Enhanced hydrogen storage efficiency with sorbents and machine learning: a review

Environmental Chemistry Letters, Год журнала: 2024, Номер 22(4), С. 1703 - 1740

Опубликована: Май 16, 2024

Abstract Hydrogen is viewed as the future carbon–neutral fuel, yet hydrogen storage a key issue for developing economy because current techniques are expensive and potentially unsafe due to pressures reaching up 700 bar. As consequence, research has recently designed advanced sorbents, such metal–organic frameworks, covalent organic porous carbon-based adsorbents, zeolite, composites, safer storage. Here, we review with focus on sources production, machine learning. Carbon-based sorbents include graphene, fullerene, carbon nanotubes activated carbon. We observed that capacities reach 10 wt.% 6 3–5 adsorbents. High-entropy alloys composites exhibit improved stability uptake. Machine learning allowed predicting efficient materials.

Язык: Английский

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Machine learning for the advancement of membrane science and technology: A critical review

Journal of Membrane Science, Год журнала: 2024, Номер 713, С. 123256 - 123256

Опубликована: Сен. 3, 2024

Machine learning (ML) has been rapidly transforming the landscape of natural sciences and potential to revolutionize process data analysis hypothesis formulation as well expand scientific knowledge. ML particularly instrumental in advancement cheminformatics materials science, including membrane technology. In this review, we analyze current state-of-the-art membrane-related applications from perspectives. We first discuss foundations different algorithms design choices. Then, traditional deep methods, application examples literature, are reported. also importance both molecular membrane-system featurization. Moreover, follow up on discussion with science detail literature using data-driven methods property prediction fabrication. Various fields discussed, such reverse osmosis, gas separation, nanofiltration. differentiate between downstream predictive tasks generative design. Additionally, formulate best practices minimum requirements for reporting reproducible studies field membranes. This is systematic comprehensive review science.

Язык: Английский

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Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Environmental Chemistry Letters, Год журнала: 2024, Номер 22(5), С. 2293 - 2318

Опубликована: Май 21, 2024

Abstract The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in context rapid industrialization urbanization. Moreover, pollutants such as antibiotics escape conventional wastewater treatments thus discharged ecosystems, requiring advanced techniques for treatment. Here we review use artificial intelligence machine learning optimize pharmaceutical treatment systems, with focus on quality, disinfection, renewable energy, biological treatment, blockchain technology, algorithms, big data, cyber-physical automated smart grid power distribution networks. Artificial allows monitoring contaminants, facilitating data analysis, diagnosing easing autonomous decision-making, predicting process parameters. We discuss advances technical reliability, energy resources management, cyber-resilience, security functionalities, robust multidimensional performance platform distributed consortium, stabilization abnormal fluctuations quality

Язык: Английский

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Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials

Materials, Год журнала: 2025, Номер 18(3), С. 534 - 534

Опубликована: Янв. 24, 2025

This review analyzes the current practices in data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon polymer membranes for gas separation) to tens nm (aerogels). While machine learning (ML)-based prediction screening crystalline, ordered porous are conducted frequently, porosity receive much less attention, although ML is expected excel field, which rich ill-posed problems, non-linear correlations a large volume experimental results. For micro- mesoporous solids carbons, silica, aerogels, etc.), obstacles mostly related navigation available data transferrable easily interpreted features. The majority published efforts based on obtained same work, datasets often very small. Even limited data, helps discover non-evident serves material production optimization. development comprehensive databases low-level structural sorption characteristics, as well automated synthesis/characterization protocols, seen direction immediate future. paper written language readable by chemist unfamiliar science specifics.

Язык: Английский

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Advanced catalytic cleaning membranes: Contemporary status and future prospects

Materials Science and Engineering R Reports, Год журнала: 2025, Номер 164, С. 100969 - 100969

Опубликована: Март 4, 2025

Язык: Английский

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A review on antifouling polyamide reverse osmosis membrane for seawater desalination

Environmental Research, Год журнала: 2025, Номер 274, С. 121305 - 121305

Опубликована: Март 5, 2025

Язык: Английский

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Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation

Environmental Science & Technology, Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility high tunability. Traditional trial-and-error methods material synthesis are inadequate to meet growing demands high-performance membranes. Machine learning (ML) has demonstrated huge potential accelerate design discovery membrane materials. In this review, we cover strengths weaknesses traditional methods, followed by a discussion on emergence ML developing advanced polymeric We describe methodologies data collection, preparation, commonly models, explainable artificial intelligence (XAI) tools implemented research. Furthermore, explain experimental computational validation steps verify results provided these models. Subsequently, showcase successful case studies emphasize inverse methodology within ML-driven structured framework. Finally, conclude highlighting recent progress, challenges, future research directions advance next generation With aim provide comprehensive guideline researchers, scientists, engineers assisting implementation process.

Язык: Английский

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Machine Learning for Predicting Thermal Runaway in Lithium‐Ion Batteries With External Heat and Force

Energy Storage, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 9, 2025

ABSTRACT The current study aims to predict the thermal runaway in lithium‐ion batteries using five artificial intelligence algorithms, considering environmental factors and various design parameters. Multiple linear regression, k‐nearest neighbors, decision tree, random forest are used as machine learning while neural networks deep algorithms. Nineteen experimental datasets train models. First, Pearson's correlation matrix is investigate effects of input parameters on onset time. dataset then updated include only tests with produced by an external heat source. As a result comparison among model performance prediction, it determined that tree best‐performing coefficient determination (R 2 ) score 0.9881, followed forest, networks, multiple regression modified when triggered heating compression forces. Results show this case, has R 0.9742. Finally, force range which best predicted, helpful conducting obtain reliable results.

Язык: Английский

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