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

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125592 - 125592

Published: July 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.

Language: Английский

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Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 63, P. 105486 - 105486

Published: May 30, 2024

Wastewater treatment plants (WWTPs) play a crucial role in ensuring safe environment by effectively removing contaminants and minimizing pollutant discharges. Compliance with stringent regulations the search for sustainable processes pose new challenges provide opportunities innovative solutions. These solutions include using wastewater as resource to recover value-added by-products, such clean water, renewable energy, nutrients, while optimizing energy consumption reducing operating costs without compromising performance. To drive continuous innovation treatment, integration of advanced technologies robust monitoring control systems is imperative. This review explores advancements automation process within WWTPs. In this context, Internet Things (IoT), cloud computing, big data analytics, artificial intelligence (AI), blockchain, robotics, drones, virtual/augmented reality (VR/AR), digital twin are identified promising tools developing innovative, smart, efficient systems. While these offers many benefits, further research essential optimize their performance cost-effectiveness. A detailed overview current future applications smart provided, emphasizing strengths, limitations, improvements.

Language: Английский

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Use, Potential, Needs, and Limits of AI in Wastewater Treatment Applications

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 170 - 170

Published: Jan. 10, 2025

Artificial intelligence (AI) uses highly powerful computers to mimic human intelligent behavior; it is a major research hotspot in science and technology, with an increasing number of applications wider range fields, including complex process supervision control. Wastewater treatment example involving many uncertainties external factors achieve final product specific requisites (effluents prescribed quality). Reducing energy consumption, greenhouse gas emissions, resources recovery are additional requirements these facilities’ operation. AI could extend the purpose expected results previously adopted tools present operational approaches by leveraging superior simulation, prediction, control, adaptation capabilities. This paper reviews current wastewater field discusses achievements potentials. So far, almost all sector involve predictive studies, often at small scale or limited data use. Frontline aimed creation AI-supported digital twins real systems being conducted, few encouraging but still applications. aims identifying discussing key barriers adoption field, which include laborious instrumentation maintenance, lack expertise design software, instability control loops, insufficient incentives for resource efficiency achievement.

Language: Английский

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Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 113152 - 113152

Published: May 23, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 944, P. 173999 - 173999

Published: June 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.

Language: Английский

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Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Water Science & Technology, Journal Year: 2024, Volume and Issue: 90(3), P. 731 - 757

Published: July 26, 2024

Artificial intelligence (AI) is increasingly being applied to wastewater treatment enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, major findings of various AI models in the three key aspects: prediction removal efficiency for both organic inorganic pollutants, real-time monitoring essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, conductivity), fault detection processes equipment integral treatment. The accuracy (

Language: Английский

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

Environmental Chemistry Letters, Journal Year: 2024, Volume and Issue: 22(5), P. 2293 - 2318

Published: May 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

Language: Английский

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Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 320, P. 100618 - 100618

Published: July 17, 2024

Language: Английский

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Compensatory measures to reduce GHGs in wastewater treatment plants in Southern Italy

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 60, P. 105128 - 105128

Published: March 14, 2024

This study evaluates Greenhouse Gas (GHGs) emissions in 183 Wastewater Treatment Plants (WWTPs) located the Apulia region Southern Italy. All WWTPs examined treat municipal wastewater. GHGs from each treatment unit were estimated current situation and compared, for same WWTPs, to those emitted after assuming structural compensatory measures mitigate them. Total estimation have been equal 83 kg CO2eq/PE⋅y 62 upgrade. Some also discussed lower emitted: recirculation of sludge thickened secondary; reduction biogas systems leakage, aerobic digester thickener coverage new system recovery anaerobic generating energy. upgrade considered result electrical energy significant emission especially digestion based WWTPs.

Language: Английский

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Challenges and requirements of AI-based waste water treatment systems

at - Automatisierungstechnik, Journal Year: 2025, Volume and Issue: 73(1), P. 40 - 49

Published: Jan. 1, 2025

Abstract Artificial Intelligence (AI) has emerged as a promising tool for enhancing the efficiency, accuracy, and sustainability of water treatment systems. However, integrating AI into comes with its own set challenges, specific requirements must be met to fully utilize potential these techniques. This study delves complexities associated implementing in waste (WWT) necessary prerequisites developing effective AI-based solutions. The most commonly utilized techniques WWT applications fall under umbrella supervised Machine Learning (ML). Supervised ML models serve excellent tools (correlation coefficient >0,8) modeling, predicting, optimizing processes. They have wide range applications, including data cleansing, system design, control optimization predictive maintenance. are particularly useful process parameters significant energy savings achievable (up 30 % reported literature). main challenges implementation such are: quality availability, efficient management along chain choice appropriate models. These highlighted two concrete examples field reuse microalgae cultivation maintenance cooling towers. showcase diverse use cases machine learning, especially wastewater applications.

Language: Английский

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