Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach

Journal of Molecular Liquids, Год журнала: 2024, Номер 410, С. 125592 - 125592

Опубликована: Июль 20, 2024

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.

Язык: Английский

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Integrating machine learning regression and classification for enhanced interpretability in optimizing the Fenton process for real wastewater treatment conditions

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 132182 - 132182

Опубликована: Фев. 1, 2025

Язык: Английский

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Molecular Simulation and ANN modelling for Cadmium (Cd) and Lead (Pb) Adsorption from Water using Zeolites

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104517 - 104517

Опубликована: Фев. 1, 2025

Язык: Английский

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Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

The Science of The Total Environment, Год журнала: 2024, Номер 944, С. 173999 - 173999

Опубликована: Июнь 13, 2024

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in technologies, necessitating development of more effective mitigation strategies. Mathematical models, widely employed predicting performance, generally exhibit low accuracy suffer from uncertainties due complex variable nature wastewater. To overcome these limitations, numerous studies proposed artificial intelligence (AI) modeling accurately predict technologies' performance mechanisms. This approach aims provide simulations predictions, thereby enhancing process control, optimization, intensification. literature review explores recent advancements membrane-based processes through AI models. The analysis highlights enormous potential this field efficiency technologies. role defining optimal operating conditions, developing strategies mitigation, novel improving fabrication techniques is discussed. These enhanced optimization control driven by ensure improved effluent quality, optimized consumption, minimized costs. contribution cutting-edge paradigm shift toward examined. Finally, outlines future perspectives, emphasizing that require attention current limitations hindering integration plants.

Язык: Английский

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Machine learning (ML): An emerging tool to access the production and application of biochar in the treatment of contaminated water and wastewater

Groundwater for Sustainable Development, Год журнала: 2024, Номер 26, С. 101243 - 101243

Опубликована: Июнь 17, 2024

Язык: Английский

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Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Water, Год журнала: 2023, Номер 15(19), С. 3325 - 3325

Опубликована: Сен. 22, 2023

Artificial Intelligence (AI) has recently emerged as a powerful tool with versatile applications spanning various domains. AI replicates human intelligence processes through machinery and computer systems, finding utility in expert image speech recognition, machine vision, natural language processing (NLP). One notable area limited exploration pertains to using deep learning models, specifically Recurrent Neural Networks (RNNs), for predicting water quality wastewater treatment plants (WWTPs). RNNs are purpose-built handling sequential data, featuring feedback mechanism. However, standard may exhibit limitations accommodating both short-term long-term dependencies when addressing intricate time series problems. The solution this challenge lies adopting Long Short-Term Memory (LSTM) cells, known their inherent memory management ‘forget gate’ In general, LSTM architecture demonstrates superior performance. WWTP data represent historical influenced by fluctuating environmental conditions. This study employs simple construct prediction models effluent parameters, systematically assessing performance training scenarios model architectures. primary objective was determine the most suitable dataset model. revealed that an epoch setting of 50 batch size 100 yielded lowest root mean square error (RMSE) values RNN models. Furthermore, these applied predict they precise RMSE all parameters. results can be detect potential upsets operations.

Язык: Английский

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Challenges and Future of Biotechnology-Driven Wastewater Treatment for Sustainable Industrial Management

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 485 - 516

Опубликована: Янв. 16, 2025

Wastewater treatment using biotechnological approaches provides environmentally friendly ways of treating industrial effluents for pollution control and resource realization. The capability microbial consortia, enzymatic machinery genetically modified microorganisms to metabolize high molecular weight compounds including heavy metals, synthetic chemicals toxic organic substrates outlines this approach. However, there are issues with the concept being implemented such as operation problems, problems scalability, question exactly how species environment. new technologies include metagenomics, biology, hybrid systems bio physical that generally improve efficacy pollutant removal.

Язык: Английский

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Insights into the application of explainable artificial intelligence for biological wastewater treatment plants: Updates and perspectives

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110132 - 110132

Опубликована: Янв. 31, 2025

Язык: Английский

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Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach

Chemosphere, Год журнала: 2024, Номер 357, С. 141868 - 141868

Опубликована: Апрель 7, 2024

Язык: Английский

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Hybrid modelling of nitrogen removal by biofiltration using high-frequent operational data

Water Science & Technology, Год журнала: 2024, Номер 90(5), С. 1416 - 1432

Опубликована: Авг. 27, 2024

ABSTRACT In this research, a parallel hybrid model is presented for the simulation of nitrogen removal by submerged biofiltration very large-size wastewater treatment plant. This combines mechanistic and machine learning to produce accurate predictions water quality variables. The models are calibrated validated using detailed quality-controlled operational data collected over period 3.5 months in 2020. modified activated sludge that describes biological, physical chemical processes taking place biofilm reactor based on domain knowledge these processes. A three-layer feed-forward artificial neural network with rectified linear activation function aims reduce model's residual error then correct its output. results show how outperforms significantly reduces size prediction errors effluent nitrate concentration from relative mean 12% (mechanistic model) 2% (hybrid during training. simulations increases 8% testing, still lower than model. These support future applications models, such as digital twins.

Язык: Английский

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