Optimizing Photocatalytic Dye Degradation: A Machine Learning and Metaheuristic Approach for Predicting Methylene Blue in Contaminated Water

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103538 - 103538

Опубликована: Дек. 1, 2024

Язык: Английский

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Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes

Energy Conversion and Management, Год журнала: 2025, Номер 327, С. 119544 - 119544

Опубликована: Янв. 24, 2025

Язык: Английский

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Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104158 - 104158

Опубликована: Янв. 1, 2025

Язык: Английский

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Green Synthesis of nZVI-Modified Sludge Biochar for Cr(VI) Removal in Water: Fixed-Bed Experiments and Artificial Neural Network Model Prediction

Water, Год журнала: 2025, Номер 17(3), С. 341 - 341

Опубликована: Янв. 25, 2025

The contamination of aquatic environments with hexavalent chromium (Cr(VI)) poses significant environmental and public health risks, necessitating the development high-performance adsorbents for its efficient removal. This study evaluates potential green-synthesized nanoscale zero-valent iron-modified sludge biochar (TP-nZVI/BC) as an effective adsorbent Cr(VI) removal through isothermal adsorption experiments, fixed-bed column studies, artificial neural network (ANN) modeling. Fixed-bed experiments demonstrated that breakthrough time, exhaustion unit capacity increased bed height. Conversely, these parameters decreased higher influent concentrations flow rates. Breakthrough curve analysis revealed Thomas model provided best fit experimental data (R2 = 0.992–0.998). An ANN model, developed using Levenberg–Marquardt algorithm, employed a single hidden layer six neurons exhibited excellent predictive performance 0.996, MSE 0.520). was validated ability to predict behavior under untested conditions, demonstrating applicability process optimization. highlights superior TP-nZVI/BC establishes theoretical basis optimizing scaling up systems findings provide valuable insights into practical application sustainable materials in remediation.

Язык: Английский

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Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review

Journal of Environmental Science and Health Part A, Год журнала: 2025, Номер unknown, С. 1 - 16

Опубликована: Фев. 2, 2025

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 understand better current status, future possibilities, capabilities of ML supporting environmentally friendly development applications. Previous applications were classified into three categories according their objectives: material process performance prediction sustainability evaluation. helps optimize BDMs systems, predict properties performance, reverse engineering, data difficulties evaluations. Ensemble models cutting-edge Neural Networks operate satisfactorily on these datasets easily generalized. neural network poor interpretability, there not been any studies assessment that consider geo-temporal dynamics; thus, building methods is currently practical. Future research should follow workflow. Investigating potential system optimization, evaluation sustainable requires further investigation.

Язык: Английский

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Enhanced fluoride removal by modified water hyacinth: response surface methodology and machine learning approach

Biomass Conversion and Biorefinery, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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Machine Learning Algorithms for Prediction of Sb Adsorption by Iron-Based Materials And Exploration of Critical Factors

Опубликована: Янв. 1, 2025

Язык: Английский

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Optimizing Water Purification: Adsorbent Performance of Phumdi Biomass Activated Carbon for Fe(II) Removal Using Artificial Neural Network

Water Air & Soil Pollution, Год журнала: 2025, Номер 236(5)

Опубликована: Апрель 4, 2025

Язык: Английский

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Sustainable water purification: evaluating Phumdi biomass adsorbent performance through machine learning-based feature analysis

Clean Technologies and Environmental Policy, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

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Emerging strategies in the sustainable removal of antibiotics using semiconductor-based photocatalysts

Beilstein Journal of Nanotechnology, Год журнала: 2025, Номер 16, С. 264 - 285

Опубликована: Фев. 25, 2025

In the constantly growing field of environmental sustainability, threat newly discovered pollutants, particularly antibiotics, has become a crucial concern. The widespread presence these pharmaceutical substances in water sources presents complex hazard to human health and ecological balance, requiring immediate novel intervention techniques. Regarding this, semiconductor-based photocatalysts have appeared as promising candidates, providing sustainable efficient way remove antibiotics from aquatic ecosystems. Nanomaterials can effectively precisely break down neutralize antibiotic compounds with high efficiency selectivity by utilizing interaction between radical reactive oxygen species non-radical equivalents under light irradiation. Although certain drawbacks, such limited capacity absorb concerns about catalytic stability, photocatalysis outperforms other advanced oxidation processes multiple aspects. This study focuses on summarizing recent advances removal using photocatalysts. By reviewing latest studies technologies, this new insights into relationship contaminants degradation processes. Compared single binary photocatalysts, modified ternary composites were found superior photodegradation performance visible exposure. To be specific g-C3N4-based exhibited more than 90% tetracycline sulfamethazine within one hour addresses during photocatalytic suggests approaches improve scalability for wider use real-world situations.

Язык: Английский

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