Revolutionizing water treatment, conservation, and management: Harnessing the power of AI-driven ChatGPT solutions

Environmental Challenges, Год журнала: 2023, Номер 13, С. 100782 - 100782

Опубликована: Окт. 21, 2023

This study analyzes the global water crisis and pressing need for innovation in treatment, conservation, management practices. It introduces potentially transformative solutions involving use of ChatGPT to address water-related challenges. The research underscores potential AI-Driven applications enhancing schemes through real-time insights, optimization resource usage, predictive maintenance. paper dilates upon role improving quality control, enriching decision-making systems, optimizing promoting precision irrigation. also discusses various scenarios where has been applied this regard, such as Klir Comply its integration into Rammas. further significant consumption environmental implications AI models like suggests tangible employing renewable energy sources recycling systems. Additionally, it delves ethical regulatory integrating management, covering aspects data privacy, transparency, accountability, conformity international standards. emphasizes concerted efforts construct a responsible governance model highlights importance education awareness regarding ethics regulations.

Язык: Английский

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Emerging technological solutions for the management of paper mill wastewater: Treatment, nutrient recovery and fourth industrial revolution (IR 4.0)

Journal of Water Process Engineering, Год журнала: 2023, Номер 53, С. 103715 - 103715

Опубликована: Апрель 11, 2023

Язык: Английский

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Leading-edge Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Internet of Things (IoT) technologies for enhanced wastewater treatment systems

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

The escalating global demand for clean water mandates the development of inventive approaches to wastewater treatment systems. This paper investigates incorporation cutting-edge technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Internet Things (IoT), revolutionize elevate procedures. collaborative application these technologies presents a promising avenue optimizing efficiency, sustainability, overall performance within infrastructure. play pivotal roles in predictive modeling decision-making processes plants. These facilitate real-time monitoring quality parameters, enabling dynamic adjustments protocols based on data-driven insights. adaptive nature AI ML algorithms enhances system resilience, diminishes operational costs, ensures adherence rigorous environmental standards. integration Blockchain technology introduces decentralized secure framework managing data By capitalizing inherent transparency immutability blockchain, stakeholders can trace complete lifecycle treatment, from source discharge. not only promotes accountability but also nurtures trust among regulators, utilities, public, fostering more transparent sustainable management ecosystem. Moreover, contributes establishment connected responsive Sensor networks embedded throughout process enable collection, facilitating remote control. seamless communication between IoT devices prompt identification anomalies potential failures, allowing timely intervention averting hazards. research underscores emphasizes significance their address evolving challenges contribute future.

Язык: Английский

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AI-Enabled Cognitive Predictive Maintenance of Urban Assets Using City Information Modeling—Systematic Review

Buildings, Год журнала: 2025, Номер 15(5), С. 690 - 690

Опубликована: Фев. 22, 2025

Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building urban studies is maturing rapidly, its use in predictive limited. Traditional preventive reactive strategies more prevalent facility management not intuitive, resource efficient, cannot prevent failure either underserve the asset or surplus to requirements. City Information Modeling (CIM) refers a federation BIM models accordance with real-world geospatial references, it can be deployed as an Urban (UDT) at city level, like BIM’s deployment level. This study presents systematic review 105 Scopus-indexed papers establish current trends, gaps opportunities for cognitive framework architecture, engineering, construction operations (AECO) industry. A UDT consisting CIM section University Florida campus proposed bridge knowledge gap highlighted review. The illustrates potential CNN-IoT integration improve through advance notifications. It also eliminates centralized information archiving.

Язык: Английский

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Wastewater Treatment with Technical Intervention Inclination towards Smart Cities

Sustainability, Год журнала: 2022, Номер 14(18), С. 11563 - 11563

Опубликована: Сен. 15, 2022

At this time, efforts are being made on a worldwide scale to accomplish sustainable development objectives. It has, thus, now become essential investigate the part of technology in accomplishment these Sustainable Development Goals (SDGs), as will enable us circumvent any potential conflicts that may arise. The importance wastewater management goals has been highlighted study. research focuses role fourth industrial revolution meeting for 2030. Given water is most important resource planet and since 11 17 directly related having access clean water, effective fundamental need achieving goals. age Industry 4.0 ushered variety new solutions many sectors, including manufacturing, energy, healthcare, electronics. This paper examines present creative treatment from an Industry-4.0 viewpoint, focusing big data, Internet Things, artificial intelligence, several other technologies. study correlated various concepts along with also discusses prior work carried out field help different In addition proposing way explaining operation I4.0 through systematic diagram, makes suggestions further well.

Язык: Английский

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An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants

Environmental Research, Год журнала: 2023, Номер 224, С. 115549 - 115549

Опубликована: Фев. 22, 2023

Язык: Английский

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Cyber-physical systems in water management and governance

Current Opinion in Environmental Sustainability, Год журнала: 2023, Номер 62, С. 101290 - 101290

Опубликована: Апрель 23, 2023

Water governance is facing rapid transformations as cyber-physical systems (CPS) are deployed across water-related sectors and river basins. These CPS — often considered artificial intelligence-enabled, automated or 'smart' technological promoted for improving monitoring, management of hydrological systems. We review recent applications CPS, highlighting their diverse functions the water cycle, including in rural, urban coastal settings. then focus on how smart technologies connect to people, policy ecosystems. Key our argument that integrating social ecosystem dimensions into research design will be vital sustainable governance, per a cybernetic approach. This includes consideration data requirements, end-user experience, sociopolitical environmental impacts, well acceptability, CPS.

Язык: Английский

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Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Euro-Mediterranean Journal for Environmental Integration, Год журнала: 2024, Номер unknown

Опубликована: Окт. 3, 2024

Abstract Membrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. technologies play a primary role combating water scarcity pollution challenges. However, the need more effective strategies to mitigate membrane fouling remains critical concern. Artificial intelligence (AI) modeling offers promising solution by enabling accurate predictions of fouling, thus supporting advanced mitigation strategies. This review examines recent progress application AI models, with particular focus on artificial neural networks (ANNs), simulating treatment processes. It highlights substantial potential ANNs, particularly widely studied multi-layer perceptron (MLP) other emerging configurations, accurately predict thereby enhancing process optimization efforts. The discusses both benefits current limitations AI-based strategies, analyzing studies offer valuable insights designing ANNs capable providing predictions. Specifically, it provides guidance selecting appropriate model architectures, input/output variables, activation functions, training algorithms. Finally, this connect research findings practical applications full-scale plants. Key steps crucial address challenge been identified, emphasizing revolutionize control drive paradigm shift toward efficient sustainable membrane-based treatment.

Язык: Английский

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Industrial circular water use practices through the application of a conceptual water efficiency framework in the process industry

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122596 - 122596

Опубликована: Сен. 24, 2024

Язык: Английский

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Exploring the potential of artificial intelligence in nuclear waste management: Applications, challenges, and future directions

Nuclear Engineering and Design, Год журнала: 2024, Номер 431, С. 113719 - 113719

Опубликована: Ноя. 19, 2024

Язык: Английский

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