A systematic review of industrial wastewater management: Evaluating challenges and enablers

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 348, P. 119230 - 119230

Published: Oct. 11, 2023

The study provides a systematic literature review (SLR) encompassing industrial wastewater management research from the past decade, examining enablers, challenges, and prevailing practices. Originating manufacturing, energy production, diverse processes, wastewater's handling is critical due to its potential impact environment public health. aims comprehend current state of management, pinpoint gaps, outline future prospects. SLR methodology involves scouring Scopus database, yielding an initial pool 253 articles. Refinement via search code leaves 101 articles, followed by abstract screening that reduces articles 79, finally 66 well-focused left for thorough full-text examination. Results underscore significance regulatory frameworks, technological innovation, sustainability considerations as cornerstones effective management. However, substantial impediments like; inadequate infrastructure, resource constraints necessity stakeholder collaboration still exist. highlights emerging domains, exemplified advanced technologies like nanotechnology bioremediation, alongside pivotal role circular economy principles in offers exhaustive view contemporary accentuating imperative all-encompassing approach integrates regulatory, technological, facets. Notably, identifies gaps opportunities forthcoming exploration, advocating interdisciplinary intensified collaboration. study's insights cater policymakers, practitioners, researchers, equipping them address challenges capitalize on prospects effectively.

Language: Английский

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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

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Deep learning: systematic review, models, challenges, and research directions

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(31), P. 23103 - 23124

Published: Sept. 7, 2023

Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This can provide a promising framework for higher performance and lower complexity. ongoing undergoes several rapid changes, resulting the processing of data by studies, while it may lead to time-consuming costly models. Thus, address these challenges, studies have been conducted investigate techniques; however, they mostly focused on specific approaches, such as supervised learning. In addition, did not comprehensively other techniques, unsupervised reinforcement techniques. Moreover, majority neglect discuss some main methodologies learning, transfer federated online Therefore, motivated limitations existing this study summarizes techniques supervised, unsupervised, reinforcement, hybrid learning-based addition each category, brief description categories their models provided. Some critical topics namely, transfer, federated, models, are explored discussed detail. Finally, challenges future directions outlined wider outlooks researchers.

Language: Английский

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Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review

Electronics, Journal Year: 2023, Volume and Issue: 12(5), P. 1102 - 1102

Published: Feb. 23, 2023

The greatest technological changes in our lives are predicted to be brought about by Artificial Intelligence (AI). Together with the Internet of Things (IoT), blockchain, and several others, AI is considered most disruptive technology, has impacted numerous sectors, such as healthcare (medicine), business, agriculture, education, urban development. present research aims achieve following: identify how technologies have evolved over time their current acceptation (1); extract prominent technologies, besides AI, that use today (2); elaborate on domains were this occurred (3). Based a sentiment analysis titles abstracts, results reveal majority recent publications positive connotation regard impact edge examples (the top five) IoT, 5G, 3D printing. effects technology still changing people interact corporate, consumer, professional while 5G other mobile will become highly genuinely revolutionize landscape all sectors upcoming years.

Language: Английский

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The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10543 - 10543

Published: July 4, 2023

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting management is thoroughly reviewed this work. review discusses variety subjects, such the sources utilized, models used, assessment measures adopted judge their efficacy. It assesses approaches critically points out advantages disadvantages. article also examines challenges with accessibility, interpretability models, ethical considerations prediction. report describes potential directions deep-learning research enhance predictions Incorporating uncertainty estimates into integrating many sources, developing hybrid mix other methodologies, enhancing few these. These goals can help become more precise effective, which will result better plans forecasts. Overall, useful resource academics professionals working on topic management. reviewing art, emphasizing difficulties, outlining areas future study, it lays solid basis. Communities prepare destructive floods by implementing cutting-edge algorithms, thereby protecting people infrastructure.

Language: Английский

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A review of physical, chemical and biological synthesis methods of bimetallic nanoparticles and applications in sensing, water treatment, biomedicine, catalysis and hydrogen storage

Advances in Colloid and Interface Science, Journal Year: 2023, Volume and Issue: 321, P. 103010 - 103010

Published: Sept. 30, 2023

This article provides an in-depth analysis of various fabrication methods bimetallic nanoparticles (BNP), including chemical, biological, and physical techniques. The review explores BNP's diverse uses, from well-known applications such as sensing water treatment biomedical uses to less-studied areas like breath for diabetes monitoring hydrogen storage. It cites results over 1000 researchers worldwide >300 peer-reviewed articles. Additionally, the discusses current trends, actionable recommendations, importance synthetic industry players looking optimize manufacturing techniques specific applications. also evaluates pros cons methods, highlighting potential plant extract synthesis mass production capped BNPs. However, it warns that this method may not be suitable certain requiring ligand-free surfaces. In contrast, laser ablation offer better control reactivity, especially where surfaces are critical. report underscores environmental benefits compared chemical use hazardous chemicals pose risks extraction, production, disposal. emphasizes need life cycle assessment (LCA) articles in literature, given growing volume research on nanotechnology materials. caters at all stages applies fields applying nanomaterials.

Language: Английский

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A review of hybrid deep learning applications for streamflow forecasting

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130141 - 130141

Published: Sept. 12, 2023

Language: Английский

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Rewards, risks and responsible deployment of artificial intelligence in water systems

Nature Water, Journal Year: 2023, Volume and Issue: 1(5), P. 422 - 432

Published: May 11, 2023

Language: Английский

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Deep learning in wastewater treatment: a critical review

Water Research, Journal Year: 2023, Volume and Issue: 245, P. 120518 - 120518

Published: Aug. 25, 2023

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of systems. Wastewater treatment are large, complex processes, with multiple controlling mechanisms, a high degree disturbance variability non-linear (generally stable) behavior internal recycle loops. Semi-mechanistic biochemical models currently dominate research application, data-driven deep learning emerging an alternative supplementary approach. But these modeling approaches have grown in separate communities practice, so there is limited appreciation the strengths, weaknesses, contrasts similarities between methods. This review addresses that gap by providing detailed guide to methods their application modeling. The aimed at experts who familiar established mechanistic approach, curious about opportunities challenges afforded We conclude discussion needs on value different ways open problems.

Language: Английский

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Understanding of Convolutional Neural Network (CNN): A Review

International Journal of Robotics and Control Systems, Journal Year: 2023, Volume and Issue: 2(4), P. 739 - 748

Published: Jan. 15, 2023

The application of deep learning technology has increased rapidly in recent years. Technologies increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are algorithms commonly used wide applications. CNN is often for image classification, segmentation, object detection, video processing, language speech recognition. four layers: convolution layer, pooling fully connected non-linear layer. convolutional layer uses kernel filters to calculate the input extracting fundamental features. combines two successive layers. third called output activation function defines a network, 'yes' or 'no'. most common popular functions Sigmoid, Tanh, ReLU, Leaky Noisy Parametric Linear Units. organization visual cortex greatly influence architecture because it designed resemble neuronal connections brain. Some architectures LeNet, AlexNet VGGNet.

Language: Английский

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