A strategic review on sustainable approaches in municipal solid waste management and energy recovery: Role of artificial intelligence, economic stability and life cycle assessment

Naveenkumar, R.; Iyyappan, J.; Pravin, Ravichandran; Kadry, Seifedine; Han, Jeehoon; Sindhu, Raveendran; Awasthi, Mukesh Kumar; Rokhum, Samuel Lalthazuala; Baskar, G.

doi:10.1016/j.biortech.2023.129044

Cited by 39 publications

(6 citation statements)

References 136 publications

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“…Biomass has gained significant traction as a renewable solid fuel due to its potential for environmental benefits. , With growing concerns over CO 2 emissions induced by reliance on fossil fuels, biomass has emerged as a promising avenue for curtailing CO 2 emissions and substituting for fossil fuels. However, due to its low energy density, elevated moisture content, reduced heating value, and intricate transportation and material handling processes, the readiness of biomass for utility scale power generation is still in doubt. − In line with this, recent research interests are being focused on cocombustion to optimize a low carbon potential energy scenario.…”

Section: Coal and Biomass Ash Mineral Transformation And Impactsmentioning

confidence: 99%

“…For instance, some utilities employ lower iron-level coals by regularly sampling coal to reduce slagging behavior in utility boilers . The use of firearms for slag removal has also gained wide application for offline slag management applications like AshProSM by MTI which has an advanced computational approach to predict ash formation, transport, deposition, and bonding strength to assist utilities in assessing coal quality and overall power plant performance before coal combustion. − However, these methods have not proven effective in dealing with alkali sulfate deposits due to their low friability.…”

Section: Introductionmentioning

confidence: 99%

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Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Bankefa,

Nasah,

Laudal

et al. 2024

Energy Fuels

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While coal−biomass cocombustion has gained considerable attention as a climate-sensitive power generation strategy, the detrimental impact of inorganic species present in low-rank fuels can lead to counterproductive outcomes in combustion systems. Low-rank solid fuels negatively affect heat transfer efficiency and energy conversion during steam generation, resulting in adverse environmental consequences. However, a combination of improved combustion technology and fuel quality improvements can enable the safe utilization of these fuels. This review highlights the pressing need for research in fuel quality improvement as an effective climate change mitigation strategy during the global transition to renewable energy sources. Identifying the problematic species in solid fuels and understanding the mechanisms of ash formation are critical steps toward designing effective strategies to control ash aerosol-induced fouling. The paper discusses methods for promoting clean combustion, including fuel pretreatment and cocombustion. Additionally, the review examines recent advancements in fuel improvement strategies, such as CFD-targeted-in-furnace injection (CFD-TIFI), intelligent soot blowing, and fuel additive technology, with a focus on mineral additives. Due to its high efficiency, low cost, and minimal retrofit requirements, additive technology holds promise for large-scale applications, paving the way for fuel flexibility in utility-scale power generation. The current study emphasizes the importance of fuel ash mineral transformation and characterization techniques for evaluating the effectiveness of fuel additives. Assessing the viability of utility-scale applications economically and the feasibility of a large-scale demonstration requires further research.

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Section: Coal and Biomass Ash Mineral Transformation And Impactsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Bankefa,

Nasah,

Laudal

et al. 2024

Energy Fuels

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“…Gao et al used ANN, GB, decision trees (DT), RF, K-nearest-neighbors, bagging regressor, and lasso regression to predict char, bio-oil, and gas product yields. GB provided better predictions with a R 2 > 0.90 in single output models, whereas the bagging regressor showed the best performance in multiple output models (Shen et They showed that AI can play a signi cant role not only for the prediction of reaction parameters but also the sorting process (Naveenkumar et al 2023).…”

Section: Applications In Bio-oil Researchmentioning

confidence: 99%

A comprehensive review of machine learning prediction in the production of bio-oil from lignocellulose via pyrolysis

Lee,

Choi,

Hwang

2024

Preprint

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Bio-oil produced through pyrolysis of lignocellulosic biomass has recently received significant attention due to its possible uses as a second-generation biofuel. The yield and characteristics of produced bio-oil are affected by reaction conditions (reactor type, particle size, feed rate, operating temperature, heating rate, retention time, etc.) and the type of feedstock that is used (softwood, hardwood, agricultural plant residues, miscanthus, etc.). Recently, machine learning (ML) techniques have been widely employed to forecast the performance of the pyrolysis and the characteristics of bi-oil. In this study, a comprehensive review of ML research on bio-oil has been carried out. Regression methods were most frequently employed to build prediction models. The top five ML methods for bio-oil research were random forest, artificial neural network, gradient boosting, support vector regression, and linear regression. In addition, users frequently extract features using their own knowledge and restricted datasets were employed I previous studies. We highlighted the challenges and potential of cutting-edge ML techniques in bio-oil production.

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“…Presently, the global annual growth rate of municipal solid waste (MSW) has surged from 8% to 10% [10]. MSW incineration (MSWI) technology stands as a pivotal waste-to-energy (WTE) method, offering an effective solution to challenges related to environmental sustainability [11]. As a typical industrial process [12,13], MSWI achieves WTE through a sequence of stages, encompassing fermentation, combustion, heat exchange, and gas cleaning [2,14].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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A strategic review on sustainable approaches in municipal solid waste management and energy recovery: Role of artificial intelligence, economic stability and life cycle assessment

Cited by 39 publications

References 136 publications

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

Advances in Efficient Utilization of Low-Rank Fuels in Coal and Biomass-Fired Systems: A Comprehensive Review

A comprehensive review of machine learning prediction in the production of bio-oil from lignocellulose via pyrolysis

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

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