Artificial intelligence for waste management in smart cities: a review

Environmental Chemistry Letters, Journal Year: 2023, Volume and Issue: 21(4), P. 1959 - 1989

Published: May 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.

Language: Английский

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Utilizing support vector regression modeling to predict pyro product yields from microwave-assisted catalytic co-pyrolysis of biomass and waste plastics

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 292, P. 117387 - 117387

Published: July 12, 2023

Language: Английский

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Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review

Journal of the Energy Institute, Journal Year: 2025, Volume and Issue: 119, P. 101973 - 101973

Published: Jan. 5, 2025

Language: Английский

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Biocrude production via hydrothermal liquefaction of cycas circinalis seed shell: A machine learning approach

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: Jan. 15, 2025

Hydrothermal liquefaction (HTL) is a promising thermochemical method for converting biomass into bio-crude fuel. This study explores the HTL of Cycas circinalis seed shell (CSS), focusing on impacts reaction time, feed slurry concentration, and temperature yield. Experiments were conducted at temperatures ranging from 250 to 375°C, times 10 40 minutes, concentrations between 10% 30%. A decision tree regression (DTR) model predicted optimal yield 35% 30% with high accuracy (R² = 0.9853, RMSE 0.992). Results highlight time as key factors influencing production.The was characterized using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS). Degradation kinetics CSS analyzed Coats-Redfern heating rates 5, 10, 20°C/min. Parameters such activation energy (E), rate constant, pre-exponential factor (A), enthalpy, entropy, Gibbs free determined. research advances hydrothermal technology promotes development sustainable efficient conversion processes.

Language: Английский

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Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications

Applied Energy, Journal Year: 2023, Volume and Issue: 346, P. 121350 - 121350

Published: June 9, 2023

Language: Английский

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Hydrogen production from plastic waste: A comprehensive simulation and machine learning study

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 59, P. 465 - 479

Published: Feb. 8, 2024

Gasification, a highly efficient method, is under extensive investigation due to its potential convert biomass and plastic waste into eco-friendly energy sources valuable fuels. Nevertheless, there exists gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) polypropylene (PP) capability produce hydrogen (H2) fuel. In this study comprehensive simulation using quasi-equilibrium approach based on minimizing Gibbs free has been introduced. To enhance H2 content, water-gas shift (WGS) reactor pressure swing adsorption (PSA) unit were for effective separation, increasing production 27.81 kg/h. investigate operating conditions effects three key variables gasification namely temperature, feedstock flow rate have explored sensitivity analysis. Furthermore, several machine learning models utilized discover optimize maximum capacity production. The analysis reveals that elevating temperature from 500 °C 1200 results higher up 23 % carbon monoxide (CO). However, generating above 900 does not lead significant upturn capacity. Conversely, an increase within shown decrease system both CO. Moreover, mass gasifying agent 250 kg/h be merely productive generation, almost 5 increase. Regarding pressure, yield decreases 22.64 17.4 with 1 10 bar. It also revealed more predominant effect Cold gas efficiency (CGE) compared Highest CGE Has by PP at °C. Among various models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99.

Language: Английский

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Machine learning to predict the production of bio-oil, biogas, and biochar by pyrolysis of biomass: a review

Environmental Chemistry Letters, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 5, 2024

Language: Английский

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Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 160872 - 160872

Published: Feb. 1, 2025

Language: Английский

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A systematic literature review on municipal solid waste management using machine learning and deep learning

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 24, 2025

Population growth and urbanization have led to a significant increase in solid waste. However, conventional methods of treating recycling this waste inherent problems, such as low efficiency, poor precision, high cost, severe environmental hazards. To address these challenges, Artificial Intelligence (AI) has gained popularity recent years potential solution for municipal solid-waste management (MSWM). A few applications AI, based on Machine Learning (ML) Deep (DL) techniques, been used MSWM. This study reviews the current landscape MSWM, highlighting existing advantages disadvantages 69 studies published between 2018 2024 using PRISMA methodology. The ML DL algorithms demonstrate their ability enhance decision-making processes, improve resource recovery rates, promote circular economy principles. Although technologies offer promising solutions, challenges data availability, quality, interdisciplinary collaboration hinder effective implementation. paper suggests future research directions focusing developing robust datasets, fostering partnerships across sectors, integrating advanced with traditional strategies. aligns United Nations' Sustainable Development Goals (SDG), particularly Goal 11, which aims make cities inclusive, safe, resilient, sustainable. In future, can contribute making smarter, greener, more resilient techniques.

Language: Английский

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Sustainable thermochemical plastic valorization towards a circular economy: a critical review

Green Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Emerging technologies-empowered thermochemical plastic valorization is developed for value-added products in green and practical manner, which are beneficial to achieving circular economy several UN sustainable development goals.

Language: Английский

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