Artificial intelligence for waste management in smart cities: a review

Environmental Chemistry Letters, Год журнала: 2023, Номер 21(4), С. 1959 - 1989

Опубликована: Май 9, 2023

Abstract The rising amount of waste generated worldwide is inducing issues pollution, management, and recycling, calling for new strategies to improve the ecosystem, such as use artificial intelligence. Here, we review application intelligence in waste-to-energy, smart bins, waste-sorting robots, generation models, monitoring tracking, plastic pyrolysis, distinguishing fossil modern materials, logistics, disposal, illegal dumping, resource recovery, cities, process efficiency, cost savings, improving public health. Using logistics can reduce transportation distance by up 36.8%, savings 13.35%, time 28.22%. Artificial allows identifying sorting with an accuracy ranging from 72.8 99.95%. combined chemical analysis improves carbon emission estimation, energy conversion. We also explain how efficiency be increased costs reduced management systems cities.

Язык: Английский

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Smart waste management: A paradigm shift enabled by artificial intelligence

Waste Management Bulletin, Год журнала: 2024, Номер 2(2), С. 244 - 263

Опубликована: Май 9, 2024

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste its environmental impact. However, advent Artificial Intelligence (AI) technologies offers promising avenues tackling complexities systems. This review provides comprehensive examination AI's role management, encompassing collection, sorting, recycling, monitoring. It delineates potential benefits challenges associated with each application while emphasizing imperative improved data quality, privacy measures, cost-effectiveness, ethical considerations. Furthermore, future prospects AI integration Internet Things (IoT), advancements machine learning, importance collaborative frameworks policy initiatives were discussed. In conclusion, holds significant promise enhancing practices, such as concerns, cost implications is paramount. Through concerted efforts ongoing research endeavors, transformative can be fully harnessed to drive sustainable efficient practices.

Язык: Английский

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An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair

Applied Sciences, Год журнала: 2023, Номер 13(8), С. 4971 - 4971

Опубликована: Апрель 15, 2023

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time appropriate selection scope maintenance activities are necessary not only for operation industrial but also effective planning demand own resources (spare parts, people, finances). A number studies have been conducted last decade many attempts made to use artificial intelligence (AI) techniques model manage maintenance. The aim article is discuss possibility using AI methods anticipate possible failures respond them advance by carrying out an timely manner. indirect these achieve more management activities. main method applied computational analysis simulation based on real data set. results show that preventive requires large amounts reliable annotated sensor well-trained machine-learning algorithms. Scientific technical development above-mentioned group solutions should be implemented such way they can used companies equal size with different profiles. Even relatively simple as presented helpful here, offering high efficiency at low costs.

Язык: Английский

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Machine learning in construction and demolition waste management: Progress, challenges, and future directions

Automation in Construction, Год журнала: 2024, Номер 162, С. 105380 - 105380

Опубликована: Март 16, 2024

Язык: Английский

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Photocatalytic degradation of drugs and dyes using a maching learning approach

RSC Advances, Год журнала: 2024, Номер 14(13), С. 9003 - 9019

Опубликована: Янв. 1, 2024

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior organic pollutants during catalytic degradation.

Язык: Английский

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A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Environmental Pollution, Год журнала: 2023, Номер 324, С. 121363 - 121363

Опубликована: Фев. 28, 2023

Язык: Английский

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Considering critical building materials for embodied carbon emissions in buildings: A machine learning-based prediction model and tool

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e02887 - e02887

Опубликована: Янв. 18, 2024

Construction activities discharge considerable carbon emissions, causing serious environmental problems and gaining increasing attention. For the large-scale construction area, high emission intensity, significant reduction potential, embodied emissions of buildings worth special studying. However, previous studies are usually post-evaluation ignore influences project, field. This paper focuses on critical building materials adopts machine learning methods to realize prediction at design stage. The activity data, including materials, water, energy consumption analyzed 30 influencing factors construction, management levels identified. Three algorithms (artificial neural network, support vector regression extreme gradient boosting) used develop models. proposed methodology is applied 70 projects in Yangtze River Delta region China. Results show that established model achieved interpretability (R2>0.7) small average error (5.33%), well proving its feasibility. Furthermore, an automated tool developed assist practitioners predict conveniently. operable practical can efficiently material stage, supporting effective adjustments improvement reduce construction.

Язык: Английский

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Data Science Applications in Circular Economy: Trends, Status, and Future

Environmental Science & Technology, Год журнала: 2024, Номер 58(15), С. 6457 - 6474

Опубликована: Апрель 3, 2024

The circular economy (CE) aims to decouple the growth of from consumption finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due rapid development data science (DS), promising progress has been made transition toward CE past decade. DS offers various methods achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize infrastructure needed circulate materials, provide evidence-based insights. Despite exciting scientific advances this field, there still lacks a comprehensive review on topic summarize achievements, synthesize knowledge gained, navigate future research directions. In paper, we try how accelerated CE. We conducted critical where helped with focus four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency optimizing (3) extending lifetime repair, (4) facilitating reuse recycling. also introduced limitations challenges current applications discussed opportunities clear roadmap for field.

Язык: Английский

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Temporal analysis of settlement areas and city footprints on construction and demolition waste quantification using Landsat satellite imagery

Sustainable Cities and Society, Год журнала: 2024, Номер 105, С. 105351 - 105351

Опубликована: Март 14, 2024

Municipal solid waste management has seen a surge in the use of satellite imagery decision-making processes, yet its application to analyze quantitative variations construction and demolition (C&D) remains under-investigated. This study employs multivariate analysis comprehensively assess predict C&D generation four diverse urban jurisdictions Canada (Regina) USA (Seattle, Buffalo, Philadelphia). Factors such as settlement area expansion, economic activities, population growth significantly influence rates. Stepwise regression models tailored different city types, moderately populated (Group 1) highly 2), showcase acceptable predictive capabilities. For cities, area, average humidity, GDP are identified key predictors, while for unemployment rate, building permit value prove effective indicators. These models, characterized by R² values from 0.70 0.94, provide insights distinct demographic conditions, aiding planning. research underscores importance understanding dynamics empowers policymakers agencies with evidence-based strategies centers.

Язык: Английский

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Estimation, Classification, and Prediction of Construction and Demolition Waste Using Machine Learning for Sustainable Waste Management: A Critical Review

Construction Materials, Год журнала: 2025, Номер 5(1), С. 10 - 10

Опубликована: Фев. 15, 2025

The management of construction and demolition waste is a critical concern for sustainable urban development environmental conservation. In this review, the authors provides an overview involvement machine learning techniques like support vector (SVM), artificial neural networks (ANNs), Random Forest (RF), K-nearest neighbor (KNN), deep convolutional (DCNNs), etc. in estimation, classification, prediction waste, contributing to advancement practices. observed that DCNN achieved outstanding accuracy 94% estimation classification waste. Based on authors’ observations, models are well suited or good future. This paper insights into promising future revolutionizing practices research.

Язык: Английский

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