Smart waste management: A paradigm shift enabled by artificial intelligence

Waste Management Bulletin, Journal Year: 2024, Volume and Issue: 2(2), P. 244 - 263

Published: May 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.

Language: Английский

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Lignocellulosic biofuel properties and reactivity analyzed by thermogravimetric analysis (TGA) toward zero carbon scheme: A critical review

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 22, P. 100538 - 100538

Published: Jan. 28, 2024

Biomass is an organic substance widely available in nature as a fresh or waste material considered renewable energy that aligns with the zero-carbon scheme to reduce dependency on fossil fuels. However, after conversion, biomass's physical chemical properties highly affect biofuel characteristics. A variety of instruments can be used figure out reactivity. Considering commonly adopted instruments, thermogravimetric analysis (TGA) simple, fast, and efficient way determine The TGA method has capability analyze (proximate analysis: moisture, volatile matter, fixed carbon, ash) combustion features biomass (such ignition, reactivity, etc). Most importantly, TG curvatures (TGA DTG) reveal behavior during thermodegradation process. As consequence, quality quantity analyses reactivity investigated comprehensively. Moreover, some integration artificial intelligence (AI) been studied better understand management technology for future development. outcome TGA-AI may obtain excellent result fit value R2>95 %. This study aims comprehensively review relevant research using lignocellulosic discussion this extended perspective, challenges, work.

Language: Английский

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A review of noncatalytic and catalytic pyrolysis and co-pyrolysis products from lignocellulosic and algal biomass using Py-GC/MS

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: 134, P. 51 - 64

Published: Jan. 9, 2024

Biomass pyrolysis has garnered significant attention as a sustainable energy production method utilizing various biomass feedstocks. Pyrolysate is any product generated from the process, including solid, liquid, and gas types. This review focuses on application of analytical pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) in context four modes: single feedstock pyrolysis, co-pyrolysis, catalytic co-pyrolysis to gain insights into characteristics pyrolysates. A comprehensive understanding each mode's unique products, benefits, limitations achieved by analyzing pyrolysates different feedstocks, lignocellulosic algal biomass. Moreover, this study discusses integration Py-GC/MS with techniques such density function theory (DFT), which estimating reactions' activation energies or kinetic studies concentrating reaction rate mechanism further insight mechanisms. Lastly, design experiment (DoE) are proposed for optimization obtain more assessment parameter's influence factors levels.

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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Multiple roles of humic substances in anaerobic digestion systems: A review

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 418, P. 138066 - 138066

Published: July 17, 2023

Language: Английский

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Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(2)

Published: Feb. 10, 2024

The escalating trends in energy consumption and the associated emissions of pollutants past century have led to depletion environmental pollution. Achieving comprehensive sustainability requires optimization efficiency implementation efficient management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction control applications found extensive utility various energy-related domains. utilization AI techniques for addressing challenges is favored due their aptitude handling complex nonlinear data structures. Based on preliminary inquiries, it been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes crucial position across sectors. This paper presents bibliometric analysis gain deeper insights into progression research from 2003 2023. models can be used accurately predict consumption, load profiles, resource planning, ensuring consistent performance utilization. review article summarizes existing literature development systems. Additionally, explores potential areas applying ANN system management. study demonstrates effectively address integration issues between power systems, such as solar wind forecasting, frequency control, transient stability assessment. state-of-the-art study, inferred consistently reductions exceeding 25%. Furthermore, this discusses future directions field.

Language: Английский

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Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Environmental Pollution, Journal Year: 2024, Volume and Issue: 344, P. 123386 - 123386

Published: Jan. 17, 2024

Language: Английский

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Biomass-derived carbon-based catalysts for lignocellulosic biomass and waste valorisation: a circular approach

Green Chemistry, Journal Year: 2024, Volume and Issue: 26(15), P. 8642 - 8668

Published: Jan. 1, 2024

Within a circular approach, cost-effective, tailored and robust biomass-derived catalysts to convert biomass play key role in biorefinery developments.

Language: Английский

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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: 21(12), P. 2771 - 2798

Published: March 14, 2024

Examining the game-changing possibilities of explainable machine learning techniques, this study explores fast-growing area biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified enhanced forecasting output composition from various biomass sources. argues that white-box models, which are more open comprehensible, crucial for prediction light increasing suspicion black-box models. Accurate forecasts guaranteed by these AI systems, also give detailed explanations mechanisms generating outcomes. For models to gain confidence processes enable informed decision-making, there must be an emphasis on interpretability openness. comprehensively synthesizes most critical features a rigorous assessment current literature relies authors' own experience. Explainable encourage ecologically responsible decision-making improving forecast accuracy transparency. Biochar is positioned as participant solving global concerns connected soil health climate change, ultimately contributes wider aims environmental sustainability renewable energy consumption.

Language: Английский

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Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

JOIV International Journal on Informatics Visualization, Journal Year: 2024, Volume and Issue: 8(1), P. 55 - 55

Published: March 16, 2024

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, solar power, can revolutionize the industry. Biomass biofuels have benefited significantly from implementing AI ML algorithms that optimize feedstock, enhance resource management, facilitate biofuel production. By applying insight derived data analysis, stakeholders improve entire supply chain - biomass conversion, fuel synthesis, agricultural growth, harvesting to mitigate environmental impacts accelerate transition a low-carbon economy. Furthermore, in combustion systems engines has yielded substantial improvements efficiency, emissions reduction, overall performance. Enhancing engine design control techniques produces cleaner, more efficient minimal impact. This contributes sustainability of power generation transportation. are employed analyze vast quantities photovoltaic systems' design, operation, maintenance. The ultimate goal is increase output system efficiency. Collaboration among academia, industry, policymakers imperative expedite sustainable future harness potential energy. these technologies, it possible establish ecosystem, which would benefit generations.

Language: Английский

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